Machine Learning and Neural Networks - page 16

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Machine Learning Course for Beginners (parts 6-10)

Machine Learning Course for Beginners

Part 6

• 05:00:00 In this section, the instructor discusses the concept of high variance and high bias models, where overfitting and underfitting can occur. The goal in machine learning is to have a low bias and low variance model to achieve optimal accuracy. The instructor gives examples of ensemble learning, where multiple models are trained on data and predictions are taken from each model to be voted on and ultimately determine the correct answer or prediction. The majority is often more accurate than an individual's response in ensemble learning, and the instructor explains the concept using examples such as quiz questions and election voting.
  
  
• 05:05:00 In this section, the instructor provides an overview of ensemble learning and how it can be used for classification and regression problems. In classification, the majority of votes are taken into consideration, whereas in regression, the mean or median of outputs from the base models are used for final prediction. The techniques used in ensemble learning include bagging, boosting, stacking, and cascading, and most Kaggle competition winners use some form of ensemble learning techniques. Additionally, the instructor points out that companies like Amazon and Google use these algorithms, such as XGBoost and random forest, in their own products.
  
  
• 05:10:00 section discusses the basics of bagging, an ensemble learning technique also known as bootstrap aggregation. Bagging involves randomly sampling subsets of training data and training models on each subset. The idea is to reduce overfitting and improve the accuracy of the model by using an ensemble of multiple models. The section explains how bagging works, including how to sample the data, train model subsets, and combine the predictions for better accuracy.
  
  
• 05:15:00 In this section, the speaker explains the concept of bagging, a technique for improving the accuracy of machine learning models. Bagging involves sampling data with replacement, and then training models on each sample. The individual models are then combined to form an aggregate model which produces more accurate predictions. The speaker notes that bagging is a conceptually simple technique that involves no advanced mathematics.
  
  
• 05:20:00 In this section, the speaker discusses bagging, which helps in reducing the variance of high variance and low bias base models. Bagging combines these models to create a larger, low variance, and low bias model. Row sampling is used while sampling the data, which involves sampling only the rows from the large distribution of data. It is important to note that in bagging, only row sampling is used, unlike random forest, which uses both row and column sampling. Bagging is also known as bootstrap aggregation, which involves bootstrapping and aggregating base models.
  
  
• 05:25:00 In this section, the instructor recaps the bagging technique, which is a way of reducing variance in high variance and low bias models. Bagging involves taking subsets of data for training and combining the majority of votes for classification. The instructor believes that a powerful algorithm for this is the random forest, which is a combination of decision trees, bagging, and column sampling. The decision trees make simple decisions and split nodes, while bagging involves taking subsets of the data for training. Column sampling or feature bagging is also used, taking subsets of the columns. The instructor asserts that random forests are powerful, and major companies like Google, Quora, and Amazon use them.
  
  
• 05:30:00 In this section, the machine learning instructor explains the concept of bagging and random forest. Bagging involves sampling rows with replacement and training a decision tree on the subset while random forest adds sampling of columns to the mix. This means that both rows and columns are sampled, which creates a higher chance of a good performing model due to the ensemble learning technique. The instructor also mentions the concept of out of bag (oob) points which are the left-out points after sampling and can be used for cross-validation. Finally, the instructor recaps the main differences between bagging and random forest.
  
  
• 05:35:00 In this section, the video discusses bagging and random forest techniques in machine learning. Bagging involves row sampling, which leads to a larger model, reducing variance. Random forest is the same, but with column sampling and decision trees as base models. The train complexity in decision trees is order of n log m times n times d, whereas in random forests, it is d times k times n. The video also discusses how random forests are trivially parallelized, making them easy to train. Finally, the concept of extremely randomized trees is introduced, which try out possible values to determine the threshold in decision trees.
  
  
• 05:40:00 In this section, the speaker discusses the concept of Extremely Randomized Trees as an alternative to the computationally expensive method of trying every possible value in Random Forest. By sampling a subset of columns and rows, the variance is reduced but there is a lesser chance of getting good results compared to Random Forest. The speaker also mentions the disadvantages of using Random Forest on large datasets due to its time complexity but suggests trying it out and tuning hyperparameters using grid search. They then introduce the scikit-learn API for implementing Random Forest and mention a project for fine-tuning hyperparameters.
  
  
• 05:45:00 In this section, the random forest classifier is discussed with its parameters and attributes. The first parameter is n_estimators, which is the number of decision trees that are used. The attribute selection criteria and maximum depth of the tree are also explained along with other parameters such as minimum sample required to split and maximum number of features. The use of feature importance is also mentioned in order to select important features for the model. The random forest regressor is also briefly discussed with its similarities to the classifier.
  
  
• 05:50:00 In this section of the video, the instructor discusses ensemble techniques, particularly Boosting. Boosting is another popular ensemble learning technique, and the instructor gives an overview of Gradient Boosting, Adaptive Boosting or AdaBoost, and Extreme Boosting or XGBoost. The instructor also mentions that there is a problem set available on GitHub for viewers to try, and encourages viewers to subscribe to the YouTube channel to support the creation of more free content.
  
  
• 05:55:00 In this section, the video covers the topics of bagging and boosting in machine learning. Bagging is used to reduce high variance in models by doing column and row sampling, followed by aggregation. Boosting, on the other hand, is used to reduce bias in models by additively combining weak learners into a strong model. The core idea of using boosting is to reduce bias in a highly biased model. The video provides a basic intuition of boosting using an example of training data.

Part 7

• 06:00:00 In this section, the speaker explains the core idea behind boosting and how it works to minimize error. Boosting is a supervised learning technique that involves training a model on training data (x and y) with a label for each example. The model is then used to predict the output for each input, and the difference between the predicted and ground truth value is measured to calculate the loss. The model is trained to reduce the residual error, focusing on the data that is misclassified or has a high MSE or MAE. Boosting involves fitting multiple models on the residual errors, minimizing the error by fitting onto it for each training example. The final model is the sum of all these models, weighted by alpha.
  
  
• 06:05:00 In this section, the concept of boosting is explained, which is a technique to reduce bias in machine learning models. Boosting converts weak learners into strong learners by fitting residual errors from previous models, resulting in a low bias and low variance model that performs well on the training set. However, there is a risk of overfitting if the model is too good on the training set. Several boosting techniques, such as gradient boosting, adaptive boost, and extreme boosting, are discussed. Additionally, the idea behind bagging is briefly mentioned, which is another technique to improve model performance.
  
  
• 06:10:00 In this section, the instructor explains the concept of gradient boosting, which is a boosting algorithm that converts weak learners into strong learners. Gradient boosting is a powerful algorithm used by big tech and production companies. It is a differentiable cost function that allows for derivatives to be taken to improve error under a training set. The instructor provides real-world examples of gradient boosting and discusses differentiable cost functions and their importance in the algorithm. The discussion includes the use of training data and a cost function in gradient descent, making it a useful tool in machine learning.
  
  
• 06:15:00 In this section, the speaker explains the algorithm for training a model using boosting. The algorithm involves initializing the model with a constant value, computing the residuals or pseudo-residuals, taking the partial derivative of the cost function with respect to the model, fitting base learners onto the residuals from previous models, and then iterating through each model to compute the pseudo-residuals and fit the base learners. The goal is to find the lambda value that minimizes the cost function and improves the model's accuracy.
  
  
• 06:20:00 In this section, the speaker explains the process of the gradient boosting algorithm, which starts by initializing the model with some constant and then applying a for loop to take out the residuals for each training example. The model then fits a base learner to the residuals and computes the multiplier lambda m for each model. To update the model, the previous model is fitted to the previous residuals, and the final model is obtained by adding the previous model and the new model obtained after solving the one-dimensional optimization problem. The speaker also covers the concepts of regularization and shrinkage and why they are needed in boosting due to the high bias.
  
  
• 06:25:00 In this section, the video discusses how boosting can be used to reduce high bias in machine learning models. Boosting involves fitting the previous model residuals during each iteration, which can result in overfitting and an increase in variance. To avoid this problem, regularization and shrinkage can be added using a learnable parameter called v. Empirically, it has been found that a value of v equals to 0.1 results in dramatic improvements. The video also covers the time complexity of gradient boosting decision trees and the implementation of gradient boosting via the scikit-learn API.
  
  
• 06:30:00 In this section, the speaker discusses the implementation of the Gradient Boosting Classifier using Scikit-learn API. They explain the different parameters involved, such as loss, learning rate, number of estimators, and more. The learning rate is used to reduce over-variance and prevent overfitting. The implementation of Gradient Boosting Classifier is just one line of code, and the predict probability gives the probability of the data being true for a certain class. The speaker also briefly discusses the implementation of Gradient Boosting Regressor and emphasizes the importance of learning from documentation.
  
  
• 06:35:00 In this section of the "Machine Learning Course for Beginners" video, the instructor discusses the AdaBoost classifier and its implementation using Cython API, as well as the Extreme Gradient Boosting (XGBoost) algorithm. The instructor explains that XGBoost is an advanced version of Gradient Boosting that adds randomization through row and column sampling, making it a powerful tool for machine learning problems. The section also covers the different parameters used in XGBoost and their significance in fine-tuning the model.
  
  
• 06:40:00 In this section, the speaker talks about the different packages for machine learning algorithms such as Gradient Boosting Tree, GB Linear, and DART. They discuss the different parameters that can be adjusted, including evaluation matrix and regularization, and how they affect the model. The speaker also mentions XGBoost and shows how it's used in Python. They emphasize the importance of fine-tuning the model and how it can lead to better accuracy. Lastly, the speaker introduces the concept of stacking and how it can help improve the accuracy of models.
  
  
• 06:45:00 In this section, the instructor introduces the concept of stacking and how it differs from bagging and boosting. Stacking involves taking different base learners that are highly-tuned and have a good bias and variance trade-off, and training them on different data subsets to create different models. This is different from bagging, which is used to reduce high variance by using base learners with high variance and low bias, and boosting, where the base models are not necessarily highly tuned. The instructor provides an example of stacking with different base models, such as logistic regression, support vector machines, and k-nearest neighbors, which have undergone extensive fine-tuning to produce good models with a good bias-variance tradeoff.
  
  
• 06:50:00 In this section, the speaker explains the basic intuition behind stacking, which is a type of ensemble learning. Stacking involves dividing the training data into subsets and training different classifiers on each subset. These base learners are good at balancing bias and variance. Unlike bagging, which has high variance and low bias, and boosting, which has high bias and low variance. After obtaining the predictions from each model, a meta-classifier is trained on the predicted class labels or their probabilities. The idea is to combine the predictions of these ensemble models to create a more accurate and robust classifier.
  
  
• 06:55:00 In this section, the instructor discusses stacking, a method of combining multiple models to create a new model with better performance. The process involves training multiple base models and using their predictions as features to train a second-level classifier, which outputs the final predictions. The instructor shows an example of creating a stacked classification model using logistic regression, k-nearest neighbors, Gaussian naive Bayes, and random forest models in Python using the sklearn library. They also demonstrate how to use the stacking classifier from the mlx10 library.

Part 8

• 07:00:00 In this section, the speaker explains how to implement a stacking classifier using different models such as K neighbors, random forest, and logistic regression. They walk through instantiating the object and performing three-fold cross-validation to select the best model with the highest accuracy. The speaker also demonstrates how to plot decision boundaries and use grid search to tune the bias and variance trade-off. By selecting the best parameters and features, the stacking classifier can provide a more accurate prediction than individual models.
  
  
• 07:05:00 In this section, the instructor summarizes the topics covered in the previous sections on ensemble learning, including bagging, random forests, boosting, gradient boosting decision trees, AdaBoost, and XGBoost. The instructor also gives an overview of stacking and provides examples of the algorithm in action. The section wraps up with a reminder to subscribe to the instructor's YouTube channel and information on a machine learning course, CS01, that covers topics beyond ensemble learning, including neural networks, GANs, and convolutional neural networks. Finally, the instructor teases upcoming sections on unsupervised learning and future projects.
  
  
• 07:10:00 In this section, the speaker introduces the concept of unsupervised learning, which involves only having access to data points without labels or a supervisor to guide the learning process. Unlike supervised learning, where the output is known, unsupervised learning involves making clusters of the data points to better understand them. As a motivating example, the speaker suggests segmenting customers in a company like Amazon based on similarities, even though there are no labels indicating which customer belongs to which segment. The goal of unsupervised learning is to discover patterns and structure in the data set.
  
  
• 07:15:00 In this section, the instructor discusses unsupervised learning and its applications. He explains that data scientists can use unsupervised learning to divide customers into segments and provide recommendations for products based on their activity on the website. He uses Amazon as an example of a company that uses unsupervised learning for customer segmentation and recommendation engines. The instructor also explains that unsupervised learning can be used for clustering in high-dimensional spaces, with similar items being close to each other and dissimilar items far away. He gives examples of sequence analysis in biology and grouping similar clusters in business as applications of unsupervised learning. Overall, the instructor provides a brief overview of unsupervised learning and its potential applications in various industries.
  
  
• 07:20:00 In this section, the speaker discusses different applications of machine learning, such as grouping similar clusters in business data for targeted marketing and using recommendation engines to suggest products based on user activity. The speaker also mentions image segmentation for object detection, sentiment analysis for determining whether a text is positive or negative, and anomaly detection for finding outliers in a model. These various applications demonstrate the versatility of machine learning in different fields.
  
  
• 07:25:00 In this section, the speaker introduces the topic of clustering and the different types of clustering algorithms such as center-based and density-based. The focus will be on the k-means clustering algorithm, which will be explored in-depth. The speaker also encourages viewers to work on problem sets and projects to gain a better understanding of machine learning. The speaker highlights the importance of unsupervised learning and shows how clustering can be applied in various fields. Clustering on an X and Y plane is used to illustrate the concept of clustering. Overall, the section highlights the upcoming topics to be covered in the course and encourages viewers to keep learning.
  
  
• 07:30:00 In this section, the speaker explains unsupervised learning and clustering, which involves segmenting data into different clusters. The terminology of intra-cluster and inter-cluster distance is discussed, where intra-cluster refers to the distance between data points inside a cluster, while inter-cluster refers to the distance between clusters. The goal is to have a small intra-cluster distance and a large inter-cluster distance, which means that data within clusters should be similar, and data between clusters should be dissimilar. This is known as the objective of optimization.
  
  
• 07:35:00 In this section, we learn about evaluation techniques for clustering or unsupervised learning models. The first technique introduced is the Dunn index, which represents the largest distance between clusters divided by the minimum distance within clusters. The goal is to have a high Dunn index, meaning that the distance between clusters should be large, while the distance within clusters should be small. This technique allows us to evaluate the quality of our clustering model.
  
  
• 07:40:00 In this section, the instructor discusses evaluation techniques for clustering models. The focus is on the Dunn index, which is an evaluation matrix used to determine if the intra-cluster is large and the inter-cluster is small. The instructor provides the basic definition of the Dunn index, which involves assessing the maximum distances between different data points within and between clusters. Another evaluation technique discussed is the Davies-Bouldin index, which is similar to the Dunn index, but with more constraints. The instructor also provides a one-line definition of clustering, which is grouping objects or elements together in a specific way.
  
  
• 07:45:00 In this section, the speaker explains the basic definition of clustering, which is the process of organizing data into groups based on similarities and differences. There are two cases in clustering: intra-cluster and inter-cluster, which measure the distance within a cluster and the difference across all the clusters, respectively. The speaker then discusses different types of clustering, including partitional-based clustering, which splits the data into two clusters, and hierarchical clustering, which uses dendrograms to visualize the clustering process. The speaker goes into more detail about agglomerative clustering and divisive clustering within hierarchical clustering, providing an example of a dendrogram to illustrate the process.
  
  
• 07:50:00 In this section, the instructor covers different types of clustering including partitional-based, hierarchical, well-separated clusters, center-based, and density-based. The instructor explains that clustering is all about grouping similar objects in a way that objects within a cluster are similar to each other and objects between clusters are different. The instructor also explains how to evaluate the performance of clustering models using different indexes, including the Dunn index and Davis-Bouldin index. The next section will focus on k-means clustering, one of the center-based algorithms.
  
  
• 07:55:00 In this section, the instructor recaps the previous subsections which covered unsupervised learning applications, clustering types, and the intuition and formal definition of clustering. The focus then shifts to the k-means clustering algorithm, also known as Lloyd's algorithm, and its various features such as initialization, centroids, hyperparameters, evaluation metrics, and limitations. The instructor provides a visualization of the algorithm with two randomly initialized centroids and illustrates the assignment step followed by the averaging step in the first iteration.

Part 9

• 08:00:00 In this section, the instructor explains the k-means clustering algorithm in detail. The algorithm involves initializing k centroids, cluster assignment, updating the clusters by taking out the average, and updating the centroid by assigning the nearest data point. This process is repeated until the centroids are not changing, indicating that the algorithm has converged. The instructor also mentions that k-means clustering is also called Lloyd's algorithm and involves randomly initializing centroids.
  
  
• 08:05:00 In this section, the speaker explains the steps for the k-means clustering algorithm. They first select the number of clusters (k), and then assign each point to the closest cluster. They recompute the centroid by taking the average and moving it, then repeat the process until the centroids stop changing. The optimization objective is to minimize the cost function, which can be calculated using the euclidean distance between data points and cluster centroids. The cost function is also known as SSE (sum squared error) and the goal is to minimize intra-cluster variability. The speaker notes that other distance metrics besides euclidean can be used as well.
  
  
• 08:10:00 In this section, the instructor explains why the random initialization of centroids in K-means clustering can cause problems and introduces the K-means++ algorithm as a solution. K-means++ involves selecting multiple centroids and choosing the one that minimizes the sum of squared errors (SSE). The instructor also introduces the elbow method, which is used to determine the optimal number of centroids based on a plot of SSE versus the number of clusters. It is recommended to use K-means++ and the elbow method rather than random initialization for better clustering results in machine learning.
  
  
• 08:15:00 In this section, the instructor explains the evaluation technique for k-means, which involves minimizing the intra-cluster by calculating the distance between points inside the cluster using the sum square error. The initialization of centroids has an impact on the algorithm, and the k-means ++ technique is a recommended method for selecting centroids based on multiple runs with low SSE. The number of clusters can be determined using the elbow method, where the optimal k value is the point where the elbow turns. The instructor also mentions some limitations of k-means clustering, such as sensitivity to outliers, which can be solved using density-based techniques such as DBSCAN or hierarchical clustering. The time complexity of k-means clustering depends on the input size, number of clusters, and dimensions. The instructor recommends the DSA mastery course to grasp the concept of time complexity better.
  
  
• 08:20:00 cluster with p1 and p2, and then we add p4 to this cluster. Next, we merge this cluster into one, and finally add p3 to the cluster to end up with one cluster. This is an example of hierarchical clustering, which is a technique where data points are grouped together based on their similarities, forming a hierarchy of clusters. The subsection will also cover agglomerative and divisive clustering, as well as a manual computation of the algorithm.
  
  
• 08:25:00 In this section, the speaker explains hierarchical clustering and the process of converting a cluster of numbers into a hierarchy of clusters. The example given shows how clusters are attached to each other based on similarity until there is only one cluster left. The speaker then explains the two types of hierarchical clustering - agglomerative and divisive clustering, and gives an intuition behind both methods. Agglomerative clustering is a down to up approach where more similar clusters are attached together, while divisive clustering is an up to down approach where clusters are divided into smaller clusters based on similarity.
  
  
• 08:30:00 In this section, the speaker explains the basic intuition behind hierarchical clustering which involves creating a hierarchy of numbers or clusters. The clustering can be done in either an up to down or down to up approach, depending on the clustering type, agglomerative or divisive. Agglomerative clustering involves merging different clusters into one, while divisive clustering involves dividing a cluster into single groups. The speaker then goes on to explain the algorithm for agglomerative clustering which involves computing a proximity matrix and repeating the process of merging clusters and updating the matrix until all clusters are covered. Finally, the speaker provides an example of an approximate matrix with four points to illustrate the concept.
  
  
• 08:35:00 In this section, the speaker explains how to create a proximity matrix and dendrogram using an example of data points. The proximity matrix helps measure similarity between two points or clusters, while the dendrogram shows the hierarchy of clusters. The speaker highlights the three methods used to measure the similarity between clusters, namely min, max, and group average.
  
  
• 08:40:00 In this section, the instructor discusses two methods for merging clusters in hierarchical clustering: minimum and maximum. The minimum approach involves taking the similarity between two clusters as the minimum distance between any two points in the clusters. The clusters with the smallest distance are merged first, and the process continues until all points are in a single cluster. The maximum approach is similar, but it takes the similarity between two clusters as the maximum distance between any two points in the clusters. The instructor provides an example using a proximity matrix to illustrate these concepts.
  
  
• 08:45:00 In this section, the instructor explains the concept of group average intercluster similarity measure, which is another type of intercluster similarity measure. He provides an equation for it and shows a dendrogram to explain how it works. The instructor then discusses the disadvantages of the minimum distance measure, stating that it is sensitive to outliers, and suggests that learners can refer to Wikipedia pages for further understanding. He also provides time and space complexity for agglomerative clustering, which is order of n square for space and order of n square log of n or order of n cube for time complexity. Finally, he concludes the section by urging learners to practice with a lot of projects to consolidate their understanding of machine learning.
  
  
• 08:50:00 In this section, the speaker discusses the project section of the course and introduces the heart failure prediction model that will be built. The speaker explains that the model will predict whether a person will die based on various features such as age, gender, blood pressure, diabetes, and smoking. The data for this project is available at a provided link, and the speaker explains that the business objective of this project is to build a healthcare AI system that will help with the early detection of health concerns to save lives. Additionally, the speaker mentions that a spam detection system project will also be presented in the course. The speaker imports the necessary libraries, loads the data, and prints the shape of the data.
  
  
• 08:55:00 In this section, we learn about the basics of exploring the data, such as checking the shape of the data and its information. Using the info() method, we can see if there are any null values, the data type, and memory usage. We can also use the describe() method to gain insight into the statistical distribution of the numerical data. Exploratory data analysis (EDA) is an essential step in machine learning, where we ask questions to the data and find answers to aid in providing business solutions. For this binary classification problem, we will examine the distribution of classes, where '1' means the person died, and '0' means the person is alive.

Part 10

• 09:00:00 In this section of the video, the instructor discusses the issue of imbalanced data in machine learning. The code snippet shows the distribution of data where there are 203 living cases and 96 death cases, which is imbalanced. Imbalanced data means that the data is not equally distributed between the classes, and this can cause the model to be biased towards certain classes. The instructor explains that imbalanced data is a big problem in machine learning, where the model may be more prone to being trained on the majority class and predicting that class more often.
  
  
• 09:05:00 In this section, the speaker explains the concept of balanced data and how models work best with it as it is more robust and unbiased. They then go on to show various ways in which data can be analyzed, such as age distribution and filtering data based on certain conditions. The speaker demonstrates a python code to select rows where the age is above 50 and seeing if the person died or not. They use pie charts to visualize the data and answer business questions, such as the total number of death cases being two times less than living cases and that most of the age rises from 40 to 95.
  
  
• 09:10:00 In this section, the instructor goes over a code snippet in Python where they're calculating the total number of diet cases and non-diet cases. They find that out of a total of 203 cases, most of the cases are diet cases but over 50 of them are above the age of 50 and have died. The instructor then goes on to explain how they can answer more questions based on this data and visually represent the data to make it easier to understand. Finally, the instructor goes over checking the correlation between variables and provides a plot to explain what correlation means.
  
  
• 09:15:00 n this section, the instructor explains correlation and how it ranges from minus one to plus one. A variable closer to minus one means it is very similar, while a value closer to zero means there is no linear transmission. Pearson correlation is a way to determine if data is linear or not, and the closer to one the correlation is, the more positively correlated the data is. The instructor talks about perfect correlation and how the diagonals are all one, which means the squares are correlating with each variable itself. After discussing data and understanding, the instructor moves on to data set development and how to divide data into training and testing sets for validating the model works best. The instructor provides an example of feature engineering, which is adding more features with categorical variables and applying transformations on the data to insert features. An interaction term is adding the product of two features, and the instructor shows how to iterate through all columns and multiply the two columns together.
  
  
• 09:20:00 In this section, the presenter discusses the process of building a model and evaluates its accuracy, precision, recall, and confusion matrix. The presenter uses an algorithm called the optimization algorithm to narrow down a dataset with 10,000 data points and ten features. They explain how stochastic gradient descent works and how it can be used to minimize computation time. Additionally, they explain key terms such as 'true positive' and 'positive class', which are important in understanding the model's overall performance.
  
  
• 09:25:00 In this section, the speaker explains the concepts of true positive, false positive, true negative, and false negative, and how they are used to create a confusion matrix, which shows the number of correctly classified instances for positive and negative classes in a model. The speaker also discusses precision and recall, which answer different questions about the accuracy of positive predictions and their actual occurrences. The speaker demonstrates the use of logistic regression and support vector classifiers with extensive fine-tuning, as well as decision tree classifiers, using randomized search for parameter optimization. The training and test scores for each classifier are also presented.
  
  
• 09:30:00 In this section of the video, the instructor explains how to build a spam and ham detector system using a dataset downloaded from the UCI repository. The data is in table format, and the instructor reads it and separates it based on a tab, with the headers set to none and the columns labeled as 'label' and 'messages'. The goal is to classify messages as either spam or not spam (ham), and the instructor walks through the process of fine-tuning different models (such as a random forest classifier and an XGBoost classifier) to achieve this. The instructor also highlights the importance of feature selection and shows how to save the XGBoost model for future use. Overall, this is an interesting project that demonstrates how machine learning can be used to solve real-world problems.
  
  
• 09:35:00 In this section of the video, the presenter goes through the process of exploring and analyzing a dataset of text messages that were downloaded from a UCI repository. The goal is to build a machine learning model that can differentiate between spam and non-spam messages. The presenter explains that the text data needs to be converted into numbers for the model to work with, and they demonstrate how to use a text vectorizer for this. They then explore the distribution of the classes, noting that the dataset is imbalanced with more non-spam messages than spam messages. Finally, they explain the importance of cleaning the text data, as minor differences in spelling or capitalization can lead to incorrect classifications.
  
  
• 09:40:00 In this section, the instructor explains the process of text preprocessing, which involves converting all text to lowercase, replacing certain characters like 0 and 3 with meaningful text equivalents, and removing unnecessary characters. The instructor also suggests exploring stemming and lemmatization for meaningful word reduction in text. An example is given using lambda to apply text preprocessing to each message, which is then stored in a new column called "processed text."
  
  
• 09:45:00 In this section, the speaker talks about pre-processing text and applying stemming using the Porter stemmer to reduce inflection into words. The speaker also mentions feature engineering, where the hand is encoded to zero and expanded to one by calling the map method. The training set is then converted into word embeddings, which convert words into numbers using techniques like count vectorizer, tf-idf vectorizer, and bag of words. The text is converted into a sparse matrix with stored elements, which is then used in the Naive Bayes algorithm for classification. Finally, the speaker gives an example of how to test a new text by calling the count vectorizer and model to determine whether it is spam or not.
  
  
• 09:50:00 In this section of the video, the speaker demonstrates how to build a basic spam and ham detector system using natural language processing (NLP) techniques, such as count vector transformation and Naive Bayes predict. The system takes messages as input, preprocesses them, and predicts whether they are spam or non-spam. The speaker emphasizes that this is just a sample of how to work with data in NLP, and that there are various other techniques that can be used. The speaker concludes the course and congratulates viewers on completing it.

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Machine Learning for Everybody – Full Course

Machine Learning for Everybody – Full Course

00:00:00 - 01:00:00 This part of the video discusses the basics of machine learning, including supervised and unsupervised learning. It also covers the different models available and how to use them. Finally, it explains how to measure the performance of a machine learning model.

01:00:00 - 02:00:00 This part explains how to use machine learning to predict outcomes of events. It discusses linear regression, logistic regression, and support vector machines. It also explains how to use a grid search to train a machine learning model.

02:00:00 - 03:00:00 This part covers the basics of machine learning, including linear regression and backpropagation. It explains how to normalize data and fit a linear regression model using the TensorFlow library.

03:00:00 - 03:50:00 This video introduces the concepts of machine learning, including supervised and unsupervised learning. It demonstrates how to use a linear regression and a neural network to make predictions. The presenter also explains how to use machine learning to cluster data.

Part 1

• 00:00:00 In this video, Kylie Ying explains supervised and unsupervised learning models, how they work, and how to program them on Google Colab.
  
  
• 00:05:00 This 1-paragraph summary explains supervised learning, which is a type of machine learning where the computer is given a set of inputs and is asked to predict the label of a given input.
  
  
• 00:10:00 Supervised learning is the process of assigning a label to input data in order to train a machine learning model. The model will then output a prediction for the given input. Unsupervised learning is the process of using unlabeled data to learn about patterns in the data. In reinforcement learning, an agent is trained in an interactive environment based on rewards and penalties.
  
  
• 00:15:00 This video discusses machine learning, its various applications, and the various types of data it can deal with. It also covers supervised and unsupervised learning, and regression.
  
  
• 00:20:00 In this video, the instructor explains how machine learning works, and how to use it to predict outcomes in a data set. The instructor also discusses how to adjust the accuracy of a machine learning model after training.
  
  
• 00:25:00 This video discusses the concept of loss, and how it affects the performance of a machine learning model. Loss is a measure of how far a prediction from a machine learning model is from the actual label given in a given data set. There are various loss functions available, each with its own advantages and disadvantages. Finally, the video discusses how to calculate and verify the performance of a machine learning model.
  
  
• 00:30:00 The video discusses how to use machine learning to predict class labels from a data set. The data set includes 10 features, each of which corresponds to a class. Histograms are used to visually compare the distributions of the features across classes. The video concludes with a discussion of how the data might be improved.
  
  
• 00:35:00 In this video, the instructor explains how to use machine learning techniques to create a training, validation, and test set. The instructor demonstrates how to scale a data set to make the values more comparable, and then creates a function to transform x values. Finally, the instructor creates a 2d numpy array and calls the hstack function to stack the arrays side-by-side.
  
  
• 00:40:00 In this video, the instructor discusses the different machine learning models available and how to use them in code. Among the models discussed are k-nearest neighbors, linear regression, and a neural network.
  
  
• 00:45:00 In this video, instructor Alan Siegel reviews the basics of machine learning, including the use of a distance function and the use of nearest neighbor algorithms. He explains that, in binary classification, the nearest neighbor algorithm will use a "k" value to determine which point is the "plus" or "minus" label. He shows how this can be applied to a data set of car ownership and child-bearing, demonstrating how the nearest neighbor algorithm can determine which point is the "plus" or "minus" label for a given data point.
  
  
• 00:50:00 This video discusses how to use machine learning to predict a point's location. The video explains how to use a k-nearest neighbors algorithm to find the closest point. The video also explains how to use a classification report to determine the point's classification.
  
  
• 00:55:00 In this video, a machine learning model is explained. The model has an accuracy of 82 percent, a precision of 77 percent, and a recall of 89 percent. The model is described as naive bayes, which is a simple machine learning model.

Part 2

• 01:00:00 Bayes rule is a mathematical formula used to calculate the probability of events given other events have already occurred. In this example, Bayes rule is used to calculate the probability of a disease given a positive test.
  
  
• 01:05:00 This video covers the basics of machine learning, with a focus on Bayesian inference. The presenter demonstrates how to apply Bayesian inference to classification problems, and discusses the various probability distributions involved.
  
  
• 01:10:00 In this video, a rule for naive bayes is explained, and it is shown that the probability of a particular event, given a set of data, is proportional to the sum of the probabilities of the individual events.
  
  
• 01:15:00 This video explains how machine learning can be used to predict outcomes of events, such as whether or not it will rain while a soccer game is being played, or what day it is. The video then goes on to discuss logistic regression, which is a more advanced machine learning technique. The video shows how the regression line can be used to predict the likelihood of different outcomes. The video concludes with a demo of how logistic regression can be used to predict whether or not a student will pass a particular test.
  
  
• 01:20:00 In this video, the instructor explains how to use linear regression to estimate the probability of a classifier being correct. In order to do this, they first need to rewrite the equation as p equals mx plus b. This equation can take on a range of negative infinity to infinity, but must stay between zero and one. To solve for p, they remove the log of the odds, which gives them p over one minus the probability.
  
  
• 01:25:00 In this video, the presenter discusses three types of machine learning models: linear regression, logistic regression, and support vector machines. The presenter demonstrates how to use each model and provides examples of how each might be used.
  
  
• 01:30:00 In this video, the instructor discusses how machine learning works and the different types of algorithms that are available. He also discusses how to maximize the margins of a support vector machine (SVM) using data points that lie on the margin lines.
  
  
• 01:35:00 In this video, the author discusses different machine learning models, including support vector machines (SVMs), neural networks, and logistic regression. He shows that SVMs are the most accurate of the three, and that neural networks can be even more accurate than SVMs.
  
  
• 01:40:00 In machine learning, a neuron is a basic unit of representation in a neural network. The input features of a neuron are multiplied by a weight, and the sum of all these multiplied inputs is then input into the neuron. The neuron's activation function alters the linear state of its inputs based on the error associated with its predictions. The gradient descent algorithm is used to follow the slope of the quadratic function towards a lower error.
  
  
• 01:45:00 In this video, the instructor explains how machine learning works and how to program a neural network using TensorFlow. He goes on to show how to create a sequential neural network and how to calculate the loss with respect to a weight.
  
  
• 01:50:00 In this video, the presenter demonstrates how to use machine learning algorithms with TensorFlow. First, they import TensorFlow and create a neural network model. Next, they set the activation of the layers, and configure the loss and accuracy metrics. Finally, they train the model using a 100-epochs training and 32-epoch validation split.
  
  
• 01:55:00 In this video, the author explains how to train a machine learning model using a grid search. He also discusses the importance of hyperparameters and how to set them.

Part 3

• 02:00:00 This video tutorial shows how to use machine learning for prediction and classification. The video covers the basics of training a machine learning model, recording the model's history, and plotting the model's performance.
  
  
• 02:05:00 This video demonstrates how to create a least-loss model for a neural network using a technique called casting. The model performs similarly to a model using an SVM, and the video also demonstrates how to create a classification report using the network's output.
  
  
• 02:10:00 In this video, the author explains linear regression, and how to calculate the residual. The residual is the distance between the prediction and the actual data point, and is used to determine the line of best fit for the regression line.
  
  
• 02:15:00 The video discusses the concepts of linearity and independence, and shows how those assumptions can be violated in nonlinear data sets. It then goes on to discuss the assumptions of normality and homoscedasticity, and how those can be evaluated using residual plots.
  
  
• 02:20:00 The measure of mean absolute error tells us in on average how far off our predictions are from the actual values in our training set.
  
  
• 02:25:00 The mean squared error (MSE) is a measure of how well a prediction is performing, and is closely related to the mean absolute error. RMSE is calculated by taking the sum of all the squares of the residuals, and is used to measure how well a prediction is performing relative to its expected value.
  
  
• 02:30:00 This 1-hour video course covers the basics of machine learning including linear regression. The course covers the topic of residuals and how to use them to determine the best line of fit for a data set.
  
  
• 02:35:00 This video introduces the concept of machine learning and how to use various libraries and data sets. It then goes on to explain how to use a data frame to represent the data and how to analyze the data.
  
  
• 02:40:00 The video discusses how to use machine learning to predict bike counts at different times of the day. It shows how to create a training, validation, and test set, and how to use the numpy.split function to divide the data frame into different groups.
  
  
• 02:45:00 The video discusses how machine learning can be used to solve problems. The instructor provides an example of using machine learning to predict the temperature, and provides information on how to calculate the regression coefficients and score the model.
  
  
• 02:50:00 In this video, the creator demonstrates how to use machine learning to improve performance of a linear regression model on a new data set.
  
  
• 02:55:00 In this video, the presenter explains how to build a linear regression model in Python using the TensorFlow library. They explain that it is helpful to normalize the data before training the model, and then fit the model using backpropagation. They show how to plot the loss of the model over time, and how the model has converged to a good fit.

Part 4

• 03:00:00 This video explains machine learning concepts in a way that is accessible to everyone. The instructor demonstrates how to use a neural network to predict values from a data set, and demonstrates the effect of changing various parameters.
  
  
• 03:05:00 This video covers the basics of machine learning, including the history of linear regression and how to use a neural network. The presenter then demonstrates how to calculate the mean squared error for a linear regression and a neural network, and compares the results.
  
  
• 03:10:00 In this video, the instructor explains how supervised and unsupervised learning work. He discusses how a linear regression and a neural network can be used to make predictions.
  
  
• 03:15:00 In this video, the presenter explains how to use machine learning to divide data into three clusters. They then use this information to calculate new centroids and create new clusters.
  
  
• 03:20:00 This video discusses two types of machine learning: unsupervised learning, which looks for patterns in data, and supervised learning, which uses a training set to learn how to predict future outcomes. Unsupervised learning techniques include expectation maximization and principle component analysis, which reduce dimensionality by finding the principal components of the data. Supervised learning techniques include linear regression and Bayesian inference.
  
  
• 03:25:00 Machine learning is a field of data analysis that helps to make predictions about unknown data. In this course, the instructor explains how to use principle component analysis (PCA) to reduce the dimensionality of a data set. This allows for easier visualization and discrimination of data points.
  
  
• 03:30:00 In this video, the presenter introduces the concept of linear regression and its application to two-dimensional (2D) data. Next, they introduce the concept of principle component analysis (PCA), which is a technique used to reduce a data set to its most relevant dimensions. Finally, they discuss the use of unsupervised learning in machine learning.
  
  
• 03:35:00 This video discusses how to use machine learning for classification using unsupervised learning. The presenter shows how to use pandas to import data, and then plots the data against one another to see the results. They conclude by discussing how some of the data looks and suggests that clustering might be improved by using a different classifier.
  
  
• 03:40:00 The video teaches how to use machine learning algorithms to cluster data.
  
  
• 03:45:00 In this video, a machine learning expert discusses how to apply various machine learning techniques to solve specific problems. The video also covers cluster analysis and PCA.
  
  
• 03:50:00 This video explains machine learning and its various stages, including unsupervised learning. It also covers how to do clustering using k-means. The video concludes with a discussion of supervised learning and its various stages, including classification and regression.

[MetaQuotes]

TensorFlow 2.0 Crash Course

TensorFlow 2.0 Crash Course

The "TensorFlow 2.0 Crash Course" video covers the basics of neural networks and their architecture, with a focus on image classification. The instructor uses a snake game and fashion mnist dataset as examples to train the neural network through the process of adjusting weights and biases based on loss functions. The video shows the importance of data pre-processing and using activation functions, such as sigmoid and ReLU, to create more complex models. The speaker also emphasizes the significance of testing and training data and demonstrates how to load and modify image data for the model. Finally, the presenter shows how to define the architecture of a model in Keras, train it using compile and fit methods, and make predictions on specific images using "model.predict".

The second part of the video tutorial covers various aspects of creating a basic neural network that can classify fashion items and conduct sentiment analysis on movie reviews. Starting with loading and preparing data for training, the tutorial goes on to explain the importance of pre-processing data and normalizing the lengths of the input sequences. The tutorial then covers the creation of a suitable model architecture, including using different layers such as embedding and dense layers. Finally, the tutorial explains how to fine-tune hyperparameters, validate the model, save and load models, and evaluate the model's performance on external data. Overall, the tutorial provides an essential structure on which to build more advanced neural network knowledge. Also it covers different topics related to TensorFlow 2.0, including encoding data for the model, running a saved model for prediction, and installing TensorFlow 2.0 GPU version on Ubuntu Linux. In the encoding section, the presenter walks through the process of trimming and cleaning data to ensure proper word mapping, and creating a lookup function to encode the data for prediction. They then demonstrate the importance of preparing input data in the correct format for the model to process, before moving on to a tutorial on installing TensorFlow 2.0 GPU version on a Linux system, advising the audience to be patient due to the size of the downloads involved.

• 00:00:00 In this section, the instructor introduces the concept of neural networks and how they work. Neural networks are composed of interconnected layers of neurons, similar to how the neurons in our brains work. These neurons can either fire or not fire, and the connections between them determine when they fire and what other neurons they may cause to fire. A neural network works by taking input from one layer of neurons and passing it through one or more hidden layers before producing output from the final layer. The architecture of a neural network can vary depending on the type of problem it's being used to solve, but one common approach is to use a fully connected neural network where each neuron in one layer is connected to every neuron in the next layer. The instructor emphasizes the importance of understanding the math behind neural networks to be able to create successful and complex ones.
  
  
• 00:05:00 In this section, the instructor explains the basics of neural networks and how they work in solving a problem. He builds a simple neural network using four inputs and one output that is trained to keep a snake alive in a game. The input is whether there is an obstacle in front, left, and right of the snake, and the recommended direction of movement which has three different values: -1 for left, 0 for straight, and 1 for right. When given an input, the neural network gives as output either a 0 or a 1, representing whether or not the recommended direction should be followed. It is designed to follow the recommended direction if it can keep the snake alive, otherwise, it will not follow it.
  
  
• 00:10:00 In this section, the speaker discusses the architecture of neural networks and how they function. The input and output layers are connected through weights, and the output is determined by taking the weighted sum of the values multiplied by these weights, with a bias value included as well. The network is then trained by inputting a large amount of data and adjusting the biases and weights in order to produce accurate outputs. If the output is correct, no adjustments are made, but if it is incorrect, the network adjusts the weights and biases to improve accuracy.
  
  
• 00:15:00 In this section, the instructor explains the process of training a neural network, in which information is passed through the network so it can adjust weights and biases to get more correct answers. The network starts with random weights and biases and iteratively adjusts them until it achieves a high level of accuracy. Activation functions, non-linear functions that add complexity to the network, are then introduced. The sigmoid activation function in particular is described as mapping any input value between negative one and one, allowing for outputs within a certain range. This introduces more complexity and richness to the network.
  
  
• 00:20:00 In this section, the speaker discusses activation functions and their role in neural networks. These functions allow for more complexity in the model by enabling non-linear functions, which are better at approximating real-world data. Sigmoid is one of the basic activation functions that transforms the output into a range from 0 to 1. A more recently used function is the Rectified Linear Unit (ReLU), which sets negative values to 0 and makes positive values more positive, thus keeping data points within a range of 0 to positive infinity. The speaker also explains that loss functions are critical in understanding how weights and biases in models need to be adjusted. They calculate the error between the predicted output and actual output, allowing for more efficient tuning and adjustment.
  
  
• 00:25:00 In this section, the speaker explains the concept of neural networks and how hidden layers can be used to create more complex models that can solve difficult problems. The video also focuses on the importance of data and how it needs to be pre-processed and put in the correct form before being sent to an array. The speaker is working off of TensorFlow's 2.0 tutorial but adds additional information that may be confusing for those new to neural networks. The tutorial uses the fashion mnist dataset, which contains images of clothing items, as an example for image classification. The video ends by showing viewers how to install TensorFlow 2.0 and matplotlib.
  
  
• 00:30:00 In this section, the video covers the necessary packages to be installed such as tensorflow, keras, numpy, and matplotlib for graphing and showing images. The video also explains the difference between testing and training data, where about 90-80% of the data are passed to the network to train it, and the remaining data is used to test for accuracy and ensure that the network is not simply memorizing the data. The video uses Keras to split the data set into training and testing data with labels. Finally, the video gives insight into the label representation, with each image having a specific label assigned to it between 0 and 9.
  
  
• 00:35:00 In this section, the instructor demonstrates how to load and modify the image data for the TensorFlow 2.0 model. He creates a list for label names, showing what each label number represents. He then uses the Matplotlib library to display the images and explains that they are arrays of 28x28 pixels. The pixel values are divided by 255 to reduce the size of the data, making it easier to work with in the model. The modified data, consisting of decimal values, is loaded into the model, which will predict the class, i.e. label number, between 0 and 9. The instructor concludes by mentioning that he will demonstrate setting up, training, and testing the model in the next section.
  
  
• 00:40:00 In this section of the TensorFlow 2.0 Crash Course, the speaker explains the architecture of a neural network for image classification. The input is an array of 28x28 pixels with grayscale values, which is flattened into a list of 784 pixels to feed into the input layer of the neural network. The output layer has 10 neurons, each representing one of the 10 classes (0-9). The goal is to have the most activated neuron represent the predicted class. The speaker also covers the hidden layers, emphasizing that a two-layer network is possible but not ideal for more complex image recognition tasks.
  
  
• 00:45:00 In this section of the TensorFlow 2.0 Crash Course, the instructor explains the concept of hidden layers in neural networks. By adding a hidden layer with 128 neurons, the network can analyze the image and identify patterns that may help recognize the image better. The selection of 128 neurons is somewhat arbitrary, and the number of neurons for a hidden layer depends on the application. The instructor then proceeds to define the architecture or layers for the model in Keras. The architecture includes a flattened input layer, two dense or fully connected layers, and an output layer with 10 neurons and softmax activation to give the probability of the network recognizing a particular class.
  
  
• 00:50:00 In this section, the video explains the process of setting up parameters for a model and training it using the "compile" and "fit" methods in TensorFlow 2.0. This includes defining the optimizer, loss function, and metrics to be used in the compiled model before setting the number of epochs for training. The video also provides a simple explanation of what epochs are and how they influence the accuracy of the model. After running the file, the test accuracy of the model is evaluated and turns out to be 87, slightly lower than the training accuracy.
  
  
• 00:55:00 In this section, the presenter demonstrates how to use the model to make predictions on specific images. He explains that you need to use the method "model.predict" and pass in a list or an np array that includes the input shape of the images. The method will then give you a group of predictions, as it expects you to pass in a bunch of different things and predicts all of them using the model. The output is a series of different lists, each containing the model's predictions for a particular image. The presenter notes that this feature is sometimes neglected in tutorial videos, but it is important to understand how to use the model practically.

• 01:00:00 In this section, the speaker shows how to interpret and validate network predictions using the np.argmax() function which finds the index of the highest number in a list. They take the value of this function and pass it into class names to get the actual name of the predicted class. The speaker goes on to set up a basic for loop to display several images from the test images and shows the corresponding prediction for each of them. They show how this can be used to validate that the model is predicting accurately, and that the prediction makes sense in relation to the input image displayed. Finally, the speaker notes a quick fix to an error that he encountered.
  
  
• 01:05:00 In this section, the video tutorial demonstrates how to create a simple model that can classify fashion items, such as a shirt or a t-shirt, using TensorFlow 2.0. The tutorial walks through the prediction process for multiple images and how to predict for one image. The model is based on a simple classification problem and is designed to provide an overview of basic neural networks. In future videos, the content will become more advanced and cover issues with real data. While the data used in this tutorial is simple, loading and pre-processing large datasets can be challenging, and adjustments will be necessary to make them usable. However, the tutorial provides an easy-to-follow structure, offering a sound basis on which to build knowledge of neural networks.
  
  
• 01:10:00 In this section, the tutorial author explains how to load in data and prepare it for training by splitting it into training and testing sets. They introduce the concept of integer-encoded words and how they represent movie reviews. The author then explains that these numbers correspond to certain words and shows how to map these integers back to their respective words. They also demonstrate the creation of a word index that assigns a unique integer to each word in the dataset. Finally, they add special keys to the word index and explain how they will be used in later parts of the tutorial.
  
  
• 01:15:00 In this section, the speaker is explaining the process of assigning values for padding, start, unknown, and unused words to their respective keys in the training and testing dataset. They add a pad tag to make all the movie review sets have the same length by adding padding words to the end of a list to make the entire movie review the same size. To create a dictionary that allows for the integers to point to a word instead of the other way around, the reverse word index list is used to reverse the values in the keys to achieve this. Finally, the speaker explains the function to decode training and testing data into human-readable words using a joined blank string and reverse word index.
  
  
• 01:20:00 In this section, the tutor explains how to normalize or set a definite length for all the reviews by using padding tags to identify the length of reviews. The tutor explains that it is impossible to determine the length of input neurons because data set can be of different lengths, so a padding tag is used to solve this limitation. The tutor demonstrates how to use Tensorflow functions such as pre-processing and sequence to pad sequences to a specific length or normalize all sequences to a user-defined length. Finally, the tutor provides a recap of the entire section and highlights the different mechanisms used to load, encode, decode, and pre-process data.
  
  
• 01:25:00 In this section, the video instructor continues with the pre-processing of the data and finalizes it to be a consistent form that can be accepted by the model. After checking the consistency of the data, the video moves on to defining the model architecture. The instructor explains the use of various layers such as embedding, global average pooling 1D, and dense layers with different activation functions. They also discuss how the model output will be a single neuron with a value between 0 and 1, indicating the probability of the review being positive or negative. The video ends by discussing the importance of word embeddings and how they help in understanding the architecture of the model.
  
  
• 01:30:00 In this section, the speaker discusses the concept of word vectors and how the embedding layer can help group words with similar meanings together. The embedding layer generates word vectors, which are essentially coefficients in a 16-dimensional space, for every word or integer-encoded term in the input data. Initially, the layer creates 10,000 word vectors for every term and groups them randomly. The layer then determines the similarity between vectors by looking at the angle between them and tries to group similar words closer to each other. This process helps the computer understand the meaning and context of words, which is essential for accurate classification of movie reviews as positive or negative.
  
  
• 01:35:00 In this section, the video explains how the embedding layer in a neural network is used to group similar words together based on their context, rather than just their content. By looking at words around a particular word, the neural network can determine which words are related to each other and group them together in the embedding layer. The output of this layer is then scaled down using a global average pooling layer, which puts the data in a lower dimension to make it easier to compute and train the network. The video provides a diagram of how the network looks after the input data is passed through the embedding layer and the subsequent layers in the neural network.
  
  
• 01:40:00 In this section, the video covers the creation of a dense layer for pattern recognition in a neural network used for sentiment analysis. The neural network takes word vectors representing different words, averages them out, and passes them to the dense layer with 16 neurons. The dense layer looks for patterns of words and attempts to classify positive or negative reviews using the sigmoid function to output a value between 0 and 1. The model is then compiled with an optimizer, a loss function, and binary cross-entropy to calculate the difference between predicted and actual values. The data is split into validation and training sets to accurately measure the model's performance on new data during testing.
  
  
• 01:45:00 In this section, the presenter modifies the test data to be validation data, which is a subset of the training data that will be used to validate the model. He explains that hyperparameters are important in machine learning and neural networks and that users need to fine-tune individual parameters to achieve accurate results. He also explains the concept of batch size, which specifies how many movie reviews will be loaded in at once, and sets it to 512. Finally, he fits the model to the data and evaluates it on the test data, which shows an accuracy of 87%. The presenter emphasizes the importance of validation data and explains that sometimes a model may be less accurate on new data.
  
  
• 01:50:00 In this section of the video, the speaker explains how to save and load models in TensorFlow to avoid having to retrain the model every time a prediction is made, which can be especially inconvenient for larger models that can take days, weeks, or even months to train. After increasing the vocabulary size of the model, the speaker shows how to save the model using the "model.save()" function and gives it a name with a ".h5" extension, which is the extension used for saved models in TensorFlow and Keras. In future videos, the speaker plans to discuss checkpointing models and loading models in batches with different sizes of data.
  
  
• 01:55:00 In this section, the speaker explains how to save and load a model in TensorFlow 2.0. The model can be saved in binary data, which saves time when making predictions as the model does not need to be re-trained every time. To load the model, a single line of code needs to be added with the filename. The speaker then demonstrates how to test the model on external data by opening a text file in code and pre-processing the data so it can be fed to the model. It is important to note that the size of the text file should be at max 250 words to match the training data. The pre-processing includes removing unwanted characters such as commas and brackets.

• 02:00:00 In this section, the video creator discusses the process of encoding and trimming down the data to 250 words so it can be used in the model for prediction. Symbols like periods, quotes, and brackets need to be removed to ensure correct word mappings. A function is created to look up the mappings for all the words in the data and return an encoded list. Once the data is encoded, the model is used to make a prediction and the original text, encoded review, and prediction are printed. Finally, a review_in_code function is defined to convert the string data to an encoded list.
  
  
• 02:05:00 In this section of the video, the presenter runs the saved model and encounters an error due to a code encoding issue. After fixing the issue, the presenter demonstrates how to translate the review into a format that the network can understand by using a vocabulary of 88,000 words to assign corresponding indices to each word in the review. The resulting output accurately identifies the review as positive, highlighting the importance of manipulating the input data to ensure that it is in the correct form for the network to process. The video then shifts to a tutorial on installing TensorFlow 2.0 GPU version on an Ubuntu Linux system, with instructions on installing CUDA and cuDNN for GPU acceleration.
  
  
• 02:10:00 In this section, the speaker guides viewers through the process of installing TensorFlow 2.0 on their computer using the GPU version. They provide a step-by-step guide to installing NVIDIA packages, including the correct drivers, and the TensorFlow 2.0 framework itself. The speaker notes that, while the process is relatively straightforward, it may take some time to complete due to the size of the downloads involved. They also advise viewers experiencing issues with their GPU usage to uninstall the CPU version of TensorFlow and to consult the comments section for further assistance.

[MetaQuotes]

Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

In this YouTube tutorial, the presenter covers a range of topics related to Python TensorFlow for machine learning and neural network text classification. They begin by discussing the set-up process in Google Colab and the import of necessary libraries, before focusing on the Wine Reviews dataset and using Matplotlib to plot histograms of the various features. The tutorial covers machine learning concepts, including supervised learning, and the difference between qualitative and quantitative data, as well as inputs and predictions in classification scenarios such as binary and multi-class classification. Other topics covered include loss functions, neural networks, activation functions, gradient descent, and backpropagation, as well as the implementation of neural nets within TensorFlow. Finally, the presenter implements a neural net using TensorFlow for text classification, demonstrating the benefits of using packages and libraries to increase efficiency.

The second part of the video tutorial covers various aspects of machine learning with TensorFlow in Python, specifically focusing on neural network text classification. The tutorial covers splitting data into training and testing sets, creating a simple model with TensorFlow and Keras, scaling and balancing datasets, using recurrent neural networks, and using TensorFlow Hub for text classification. The tutorial emphasizes the importance of evaluating model accuracy and the use of various neural network components, such as activation functions, dropout layers, and different types of cells. The tutorial concludes by summarizing the key takeaways, including building neural networks, using TensorFlow for text classification, and working with numerical data.

• 00:00:00 In this section of the video, Kylie Ying starts off by showing viewers how to set up a new notebook in Google Colab and import necessary libraries such as NumPy, Pandas, Matplotlib, TensorFlow, and TensorFlow Hub. After importing these libraries, she uploads a dataset called "Wine Reviews" as a CSV file and reads it as a Pandas DataFrame, selecting the columns that she is interested in analyzing, such as country, description, points, and price. She decides to focus on analyzing the relationship between the description and points columns to see if a neural network model can be trained to classify whether a wine review is on the lower or higher end of the points spectrum.
  
  
• 00:05:00 In this section of the video, the speaker discusses how to drop columns in a pandas dataframe and plot a histogram using Matplotlib. They plot the points column to see the distribution of values and decide to classify the reviews as below 90 and above 90. The speaker also gives a brief explanation of machine learning, including the difference between artificial intelligence, machine learning, and data science, with machine learning being a subset of AI that focuses on solving a specific problem using data. The fields of AI, machine learning, and data science overlap and may use machine learning techniques to analyze data and draw insights from it.
  
  
• 00:10:00 In this section, the speaker introduces three different types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. The tutorial focuses on supervised learning where the input has a corresponding output label and is used to train models to learn outputs. The speaker explains that the machine learning model learns patterns in data to come up with the prediction and that the list of inputs is called a feature vector. The speaker also discusses the two different types of qualitative data inputs: categorical and finite numbers of categories or groups. The tutorial focuses on using labeled input and output pairings to make future predictions.
  
  
• 00:15:00 In this section, the video introduces the concept of qualitative and quantitative features and how they can be encoded into numbers for computers. Qualitative features, such as nationality or age groups, are those that don't have a number associated with them, and they can be encoded using one-hot encoding. Numeric features, such as the size of a desk or the temperature of fire, are quantitative and can be either continuous or discrete. The video also distinguishes between nominal data, which doesn't have an inherent ordering, and ordinal data, which have an inherent ordering, and discusses how different types of qualitative features may require different encoding techniques.
  
  
• 00:20:00 In this section, the speaker explains the different types of inputs and predictions in supervised learning. Inputs can either be continuous or discrete, while predictions can be classified as either binary or multi-class. For binary classification, the model predicts whether an input belongs to one of two categories, while multi-class classification involves mapping inputs to one of several categories. Additionally, regression is used to predict continuous values. The speaker then teases the topic of models, but doesn't go into specifics yet. Finally, the speaker briefly touches on how to make the model learn and evaluate its performance.
  
  
• 00:25:00 In this section, the speaker discusses the dataset they will be using for a real-world example and explains the structure of the data. The data set contains information on individuals, including quantitative variables such as the number of pregnancies, glucose levels, and blood pressure, as well as an outcome variable indicating whether or not they have diabetes. Each row represents a different individual, and each column represents a different feature that can be fed into the model. The feature vector is what is being plugged into the model, with the target for that feature vector being the output to be predicted. The speaker goes on to explain that the data set is split into a training data set, a validation data set, and a testing data set, and that the validation data set is used to check that the model can handle unseen data before the model is tested on the testing data set.
  
  
• 00:30:00 In this section, the speaker discusses loss functions and how they quantify the difference between predicted values and actual values in regression and binary classification scenarios. In regression, L1 loss function finds the difference between predicted and actual values, while L2 loss function squares the differences. In binary classification, binary cross-entropy loss finds the difference between predicted and actual probabilities of belonging to a particular class. The speaker also explains accuracy as a measure of performance in classification scenarios. Finally, the speaker introduces the concept of the model and how it fits in the overall machine learning process.
  
  
• 00:35:00 In this section, the speaker discusses neural networks, stating that they are popular for their ability to be used for classification and regression. However, the speaker also mentions that neural networks are sometimes overused, and using them for simple models can be unnecessary. The speaker discusses how neural networks are a black box and therefore sometimes lack transparency. They explain the structure of a neural net, including how features are multiplied by weights and added to biases before being passed through an activation function. The speaker emphasizes the importance of using a non-linear activation function to prevent the neural net from becoming a simple linear regression.
  
  
• 00:40:00 In this section, the video discusses activation functions and their role in neural networks. If a neural network simply used a linear function, it would not serve its purpose. Activation functions, such as sigmoid, tange, and relu, allow each neuron to be projected into a non-linear space, which enables the training process. The video then explains the concept of gradient descent and back propagation, which are the reasons why neural nets work. Gradient descent measures the slope of the loss function, and back propagation adjusts the weight values to minimize the loss. Through this process, a new weight value is set for each parameter, which is the old weight plus some alpha.
  
  
• 00:45:00 In this section, the video discusses the implementation of neural networks in machine learning using libraries such as TensorFlow that have already been developed and optimized. The TensorFlow library is an open-source library that helps develop and train machine learning models. The library comprises many modules, including Keras, which contains modules that aid users in the creation and optimization of models, such as different optimizers. The video also explains why it is beneficial to use established libraries rather than coding a neural net from scratch.
  
  
• 00:50:00 In this section, the presenter shows viewers how to implement a neural net using TensorFlow for text classification. They start by introducing the benefits of using packages and libraries to improve efficiency, before moving onto creating a new notebook in which they load a dataset called diabetes.csv using the read csv function. The dataset comes from the National Institute of Diabetes and Digestive and Kidney Diseases and includes various features and patient demographics as well as a classification of whether the patient has diabetes. The presenter then demonstrates how to visualize the data by plotting histograms of the various features, indexed by whether the patient has diabetes or not, using a for loop in the Jupyter Notebook.
  
  
• 00:55:00 In this section of the video tutorial on using Python TensorFlow for machine learning and neural network text classification, the presenter creates a new data frame with outcomes set to zero and another with outcomes set to one to differentiate between patients with diabetes and those without. The next step involves visualizing the number of diabetes-positive and -negative patients, which appears inconclusive since there is no separable pattern among the different values. This highlights the importance of machine learning in predicting diabetes by considering all features together. Finally, the presenter splits the data frame into x and y values for further analysis.

• 01:00:00 In this section, the speaker explains how to split the data into training and testing sets using the Scikit-learn module. The speaker imports the module, calls the train_test_split function, and passes in the input and output features. The test size is set to 20%, while the training size is set to 60% of the data set. The temporary sets are then used to create validation and test sets using a 50/50 split. Finally, the speaker builds a simple model using TensorFlow and Keras, specifically the dense layer with 16 neurons that are densely connected to the preceding layer.
  
  
• 01:05:00 In this section, the speaker explains the process of creating a neural net for binary classification using TensorFlow in Python. They add layers and an activation function called sigmoid that maps input to probabilities of whether or not something belongs to a single class. The model is then compiled with an optimizer called "adam," a loss function called "binary cross-entropy," and a metric for accuracy. Before training the model, they evaluate its performance on the training and validation data, which yields a low accuracy. Finally, the speaker trains the model using the "model.fit" function, passing in the training and validation data, batch size, and number of epochs.
  
  
• 01:10:00 In this section, the presenter discusses the need to scale the dataset features to make sure they are on a more standardized range. The presenter explains how to import the necessary package to scale the features using sklearn pre-processing and standard scalar. After scaling the features, the presenter checks the range of values and plots the transformed data frame to demonstrate that most of the features are now on a similar scale. Scaling the features helps ensure that the features are not affecting the neural network's result due to the different ranges.
  
  
• 01:15:00 In this section, the video tutorial focuses on normalizing and balancing the dataset through a process called random over-sampling. By visualizing the data, the instructor demonstrates how the dataset for diabetes patients and non-diabetes patients is highly imbalanced, leading to a neural net that may not train well. By using the random over sampler, more random samples are added into the dataset, balancing out the lengths of the two categories. This is achieved by importing another package called Imbalance learn dot over sampling which includes a Random Over Sampler. The dataset is split again using the fit_resample function making both outcomes approximately equal to one. After re-running the cells, the accuracy of the model is closer to 50%, indicating that balancing the dataset has resulted in a better model performance.
  
  
• 01:20:00 In this section, the creator evaluates the neural network model on a test dataset and achieves an accuracy of 77.5%, showing that the model is successful in generalizing to unseen data. The tutorial then moves on to discuss recurrent neural networks (RNNs) and how they are useful for data that involves sequences or series. RNNs allow for the creation of a memory within the neural network, allowing it to remember information from previous time steps. However, the use of RNNs can lead to problems such as exploding or vanishing gradients during back propagation.
  
  
• 01:25:00 In this section, the speaker discusses the issues with gradients getting closer and closer to zero, which prevents models from updating and learning. To combat this, there are different types of cells or neurons that can be used, such as the gated recurrent unit and the long short term memory unit. The speaker then moves on to a TensorFlow example with text classification using wine reviews and demonstrates how to split the data into training, validation, and test datasets. The speaker uses numpy's split function to show a different way of splitting the data and emphasizes the importance of being flexible when working with datasets.
  
  
• 01:30:00 In this section, the video tutorial covers how to split a data set into training, validation, and test sets and how to convert them into a tf.data.Dataset object in TensorFlow. The instructor uses a larger batch size and tf.data.autotune due to the large size of the data set, and adjusts the function code to change the target variable to "label" since the data has already been labeled. The tutorial also explains how the TensorFlow hub works, which is used for text classification in this tutorial. The video demonstrates how to visualize the data sets within the TensorFlow data set objects as well.
  
  
• 01:35:00 In this section, the video tutorial discusses how to use TensorFlow Hub, a repository of pre-trained machine learning models, to help with text classification. The video explains that computers don’t understand text, so the text needs to be transformed into numbers that the computer can understand. To do this transformation, the video uses nnlm, a token-based text embedding trained on English Google news, and converts all of the text into a vector of numbers. The video then shows how to build a neural network model using TensorFlow Keras with a dense layer, ReLU activation function, and a binary classification output. The video compiles the model and evaluates it on the training and validation data, showing an accuracy of around 40%.
  
  
• 01:40:00 In this section of the video, the speaker trains a machine learning model and observes the results. The training accuracy increases and the loss decreases, indicating that the model is well-trained. However, the validation accuracy plateaus and even decreases slightly, indicating that the model is not generalizing well and is overfitting. To solve this issue, the speaker suggests adding dropout layers, which randomly select a few nodes to "turn off" during each training iteration. This introduces more randomness and variability in the model, helping it to generalize better. Lastly, the speaker repeats the hub layer and compiles the model.
  
  
• 01:45:00 In this section, the user evaluates the neural network that was created and trained in the previous section. The loss is decreasing and the accuracy is increasing, but the user stops the training early after only five epochs. The model is evaluated on the test data and the accuracy is around 83%. The user then shows how to recreate the model using a LSTM and starts by creating an encoder with a max token set to 2000. The model is then defined using the encoder and an embedding layer with an input dimension set to the length of the encoder's vocabulary and an output dimension set to 32. Finally, an LSTM layer is added with 32 nodes.
  
  
• 01:50:00 In this section of the video, the instructor is building the neural network by adding a dense layer and a dropout layer to prevent overfitting. The activation function for the output is sigmoid, while the input requires a tokenizer. The accuracy of the model is found to be around 53 and the loss around 0.7. Then, the model is trained by evaluating the training and validation data. At the end of the training, the accuracy for test data is found to be 84. The tutorial ends with the instructor summarizing the takeaway, which is learning how to build a feed-forward neural network, use TensorFlow for text classification, and implement numerical data.

[MetaQuotes]

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial  (parts 1-4)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

00:00:00 - 01:00:00 This video provides an introduction to TensorFlow 2.0, a library for data manipulation and machine learning. The instructor explains what a tensor is and how to use tensors to store partially defined computations. He also demonstrates how to use the TF dot rank and TF dot reshape functions to control the number of dimensions in a tensor.

01:00:00 - 02:00:00 The video tutorial explains how to use linear regression to predict values in a data set. The Titanic data set is used as an example. The presenter explains how linear regression is used to predict values in a data set and how to create feature columns in a data set using TensorFlow.

02:00:00 - 03:00:00 This video tutorial covers the basics of using Python for neural networks. The video starts with a description of how a neural network is composed of layers of interconnected neurons. The video then covers how to create a random number generator and how to train a neural network. Finally, the video shows how to connect neurons and weights, how to pass information through the network, and how to calculate the output value of a neuron.

03:00:00 - 04:00:00 This video explains how to use TensorFlow to build a convolutional neural network for image recognition. The video covers the basics of convolutional neural networks, including how they work and how to use pre-trained models.

04:00:00 - 05:00:00 This video explains how to use TensorFlow to train a machine learning model that can predict the class of an image. The video covers basic concepts such as deep learning and Convolutional Neural Networks.

05:00:00 - 06:00:00 This video is a complete guide to using TensorFlow 2.0 for training neural networks. It covers the input and output shapes of the neural network, how to create a loss function, and how to use the model to predict a sequence. The video also demonstrates how to generate text with TensorFlow.

06:00:00 - 06:50:00 This video tutorial introduces the basics of TensorFlow 2.0, a powerful machine learning library. After introducing TensorFlow and its key concepts, the tutorial walks viewers through a series of tutorials on different machine learning tasks such as deep learning and reinforcement learning.

Part 1

• 00:00:00 This video tutorial teaches beginners how to use TensorFlow 2.0 for neural networks in Python. The instructor explains the differences between artificial intelligence, neural networks, and machine learning, and provides resources for the course, including practice exercises and code examples.
  
  
• 00:05:00 Machine learning is a subfield of artificial intelligence that allows computers to generate rules for themselves, without having to be explicitly programmed.
  
  
• 00:10:00 The video explains the basics of neural networks and deep learning, and how these systems work by transforming data through multiple layers. Neural networks can be used for a variety of tasks, including machine learning and artificial intelligence. The video also provides an example data set of student grades.
  
  
• 00:15:00 This video discusses the difference between artificial intelligence, machine learning, and neural networks, and then goes on to discuss supervised learning.
  
  
• 00:20:00 Unsupervised machine learning is used when we don't have any input data. It is used to figure out groups of data points that are similar.
  
  
• 00:25:00 This video provides an overview of three different types of machine learning: supervised, unsupervised, and reinforcement learning. The last type, reinforcement learning, is explained in more detail with an example of a game where an artificial intelligence (AI) agent tries to reach a specific goal.
  
  
• 00:30:00 In this video, a basic introduction to TensorFlow is given, followed by a discussion of how the library works on a lower level. TensorFlow is then discussed in more detail, including the two main components: graphs and sessions. A graph is a collection of partial computations that are related to each other, and a session is a way to execute part or the entire graph.
  
  
• 00:35:00 In this video, the instructor introduces the basics of TensorFlow, including the concept of graphs and sessions. He then demonstrates how to use Google Collaboratory to create and execute code blocks, as well as import and use various modules. Finally, he discusses the benefits of using TensorFlow on a computer with limited resources.
  
  
• 00:40:00 In this video, the instructor demonstrates how to import and use TensorFlow 2.0 with Python neural networks. TensorFlow is a library for data manipulation and machine learning. TensorFlow 2.0 is a new version of the library that improves performance. The instructor also explains what a tensor is and how it generalizes vectors and matrices.
  
  
• 00:45:00 TensorFlow 2.0 introduces tensors, which are important objects that store partially defined computations. Tensors have a data type and a shape, and each tensor has a rank and a degree. To determine the rank of a tensor, use the TF dot rank method.
  
  
• 00:50:00 In this video, the author introduces the concept of tensor shapes and explains how they can be used to identify the number of elements in a tensor. He also introduces the concept of tensor rank and shows how it can be used to control the number of dimensions in a tensor. Finally, he demonstrates how to reshape a vector of data into a different shape using the TF dot reshape function.
  
  
• 00:55:00 This video teaches the basics of tensors, including their different types, how to create and evaluate tensors, and how to reshape them.

Part 2

• 01:00:00 This  video explains the basic idea of linear regression, which is a machine learning algorithm used for predicting future values from past data. Linear regression is used when data points correlate in a linear fashion.
  
  
• 01:05:00 In this tutorial, you will learn how to use linear regression to predict new data points using a line of best fit.
  
  
• 01:10:00 In this video, the presenter explains how linear regression is used to predict values in a data set. They first explain how linear regression works in three dimensions and then show how to code it in TensorFlow.
  
  
• 01:15:00 The video introduces the Titanic data set and explains why linear regression is a good algorithm for predicting who will survive on the ship. The data set is loaded into pandas and Excel.
  
  
• 01:20:00 This video explains how to use a data frame to store data and how to use the dot operator to look up specific values inside the data frame.
  
  
• 01:25:00 In this video, the instructor explains the basics of Tensors and how they are used in machine learning. He then shows how to create a model using a training data set and a testing data set.
  
  
• 01:30:00 In this video, the author explains how to create feature columns in a data set using TensorFlow. These columns will be used to train a linear estimator or model.
  
  
• 01:35:00 In this video, the instructor explains how to create feature columns and numeric columns in a TensorFlow model, and how to train the model using batches.
  
  
• 01:40:00 The TF 2.0 Complete Course explains how to create a neural network using Python, and how to train and evaluate the network. The video tutorial shows how to create an input function and how to use the function to create images of the panda dataset.
  
  
• 01:45:00 This 1-paragraph summary explains how to use a TensorFlow model to make predictions on a data set. First, you create the model using syntax from the estimator module. Next, you train the model using a linear classifier object. Finally, you evaluate the model and print out the accuracy.
  
  
• 01:50:00 This video covers how to use TensorFlow to create neural networks for predicting outcomes, including how to loop through the predictions and access the actual values.
  
  
• 01:55:00 In this tutorial, the author shows how to use TensorFlow to train a neural network to predict the species of flowers from a data set. The author also explains the different types of neural networks and how to use input functions to optimize their performance.

Part 3

• 02:00:00 In this video, the author walks through the process of creating a neural network in TensorFlow 2.0, and explains the different models that are available. They then show how to build the neural network and train it using a dnn classifier.
  
  
• 02:05:00 In this video, a train and evaluate function is created for a neural network. A lambda is used to define the function in one line. The function is then used to train the neural network. The accuracy of the trained neural network is displayed.
  
  
• 02:10:00 The video shows how to use TensorFlow's prediction function to predict the class of a flower. The user first inputs the features of the flower, such as sepal length, petal length, and width. Then, they create a predictive dictionary and feed it the features. TensorFlow then predicts the class of the flower based on the input features. Finally, the user prints out the prediction and the probability of the prediction.
  
  
• 02:15:00 This video covers the basics of neural networks, including classification and clustering.
  
  
• 02:20:00 The video discusses how to use Python neural networks to cluster data points. After labeling all data points, the video explains how to find the closest center of mass between all clusters and how to reassign data points to the closest cluster.
  
  
• 02:25:00 In this video, the instructor covers the basics of neural networks and hidden Markov models. He starts by discussing data types used in machine learning, such as k-means and linear regression. He then discusses a basic weather model and how to create a hidden Markov model to predict the weather on any given day.
  
  
• 02:30:00 In this video tutorial, the presenter introduces the basics of TensorFlow, including its usage for neural networks. He then goes on to demonstrate how to create a basic weather model in TensorFlow, using two states and two probability distributions. The presenter then demonstrates how to use the model to predict the temperature for the next week.
  
  
• 02:35:00 This video demonstrates how to use TensorFlow to create a neural network to predict the average temperature on each day of a given sequence. The video explains how to import the necessary modules and create a model. The model correctly predicts the average temperature on the first day of the sequence, but predicts the wrong temperature on the subsequent days. After troubleshooting the issue, the video demonstrates how to use a different TensorFlow module to successfully predict the average temperature on each day of the sequence.
  
  
• 02:40:00 This video describes how to run a model in TensorFlow 2.0 using the Mean command. This is a helpful command for comparing the expected values of variables in a model.
  
  
• 02:45:00 In this module, the instructor will be discussing neural networks and how they work, as well as explaining how a neural network is composed of layers. He will also discuss how an input layer receives raw data and how a neural network would be able to classify that data.
  
  
• 02:50:00 The "TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial" provides a step-by-step guide to creating a basic neural network in Python. The first layer is an input neuron, followed by a hidden layer, and then an output layer. The output layer has one neuron for every piece of input information, and the neurons in the hidden layer are connected to each other and to the output layer. The hidden layer is connected to the input layer and to the output layer, which is called a densely connected neural network.
  
  
• 02:55:00 This video tutorial demonstrates how to use Python for neural networks, showing how to create a random number generator and train a neural network. The video also covers how to connect neurons and weights, how to pass information through the network, and how to calculate the output value of a neuron.

Part 4

• 03:00:00 In this video, a Python neural network is explained, with the help of an activation function. This function squishes/squares negative and positive numbers, allowing the network to more easily identify between red and blue colors.
  
  
• 03:05:00 In this video, the author explains the concept of an activation function and how it affects the output of a neural network. He also explains how an activation function can be used to introduce complexity into the network. The author then goes on to discuss a loss function, which is used to train a neural network.
  
  
• 03:10:00 This video introduces the concept of neural networks and describes the three main loss functions used in neural network training: mean squared error, mean absolute error, and hinge loss. It then goes on to explain gradient descent, which is the algorithm used to optimize neural network performance.
  
  
• 03:15:00 This video tutorial introduces the basics of TensorFlow, including how to create and train a neural network. The video also covers the use of a neural network optimization algorithm, and explains how to evaluate the performance of a neural network.
  
  
• 03:20:00 In this video, the instructor demonstrates how to use TensorFlow to train a neural network in Python. The data set used in the demonstration includes images of different types of clothing, and the instructor shows how to use the training and testing functions in TensorFlow to validate the network's predictions.
  
  
• 03:25:00 In this video, the instructor describes the basics of a neural network, including the layers and nodes in a neural network, and the activation function used to determine the output value for a neuron. The instructor also describes how to create a model in TensorFlow, and how to run the model.
  
  
• 03:30:00 In this 1-hour video tutorial, the creator covers the basics of TensorFlow, including the architecture and function of a neural network. They then go on to show how to compile and fit a neural network, and finally train it on a training set. Overfitting is explained, and the accuracy of the trained model is compared to that of a test set.
  
  
• 03:35:00 In this video, the instructor explains how neural networks can overfit and how to improve generalization by tweaking hyperparameters. He then demonstrates how to make predictions on test images using the predicted object from the neural network.
  
  
• 03:40:00 This video tutorial teaches how to use TensorFlow to build a convolutional neural network for deep computer vision. The video covers what a convolutional neural network is, how it works, and how to use pre-trained models.
  
  
• 03:45:00 The video explains the difference between a dense neural network and a convolutional neural network, and explains that a convolutional neural network learns local patterns instead of global patterns.
  
  
• 03:50:00 The Convolutional Neural Network (CNN) is a machine learning algorithm that is used to analyze and understand images. The Convolutional Neural Network is composed of a number of different layers that are used to analyze and understand the image. The first layer is used to identify the presence of specific filters in the image.
  
  
• 03:55:00 In this video, the instructor explains how to find a filter in a image using the cross product. The filter is determined based on how similar the pixel values are between the image and the filter.

[MetaQuotes]

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial (parts 5-7)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Part 5

• 04:00:00 The video discusses how convolutional neural networks work and how padding and stride help make the network easier to use.
  
  
• 04:05:00 In this video, the instructor explains how pooling operations, such as min and max, are used to reduce the dimensionality of a feature map. He then demonstrates how this is done by showing how to sample values from a feature map and generate a feature map with reduced dimensionality. Next, the instructor shows how to use a two-by-two pooling operation to reduce the size of the feature map by half. He then demonstrates how to use a max pooling operation to find features in an area. He then demonstrates how to use an average pooling operation to find features in an area. Finally, the instructor shows how to use Kerris to create a convolutional neural network.
  
  
• 04:10:00 In this video tutorial, the instructor demonstrates how to create a convolutional neural network (CNN) using Python. The first layer of the CNN is a max pooling layer that reduces the dimensionality of the input data. The next two layers are a convolutional layer and a max pooling layer. The final layer is a dense layer that classifies the input data.
  
  
• 04:15:00 This video explains how data augmentation can be used to improve the performance of a machine learning model. Data augmentation involves modifying an image to make it more similar to the training data, which can improve the model's generalization ability.
  
  
• 04:20:00 This video shows how to use pre-trained models in TensorFlow 2.0 to improve the accuracy of classifications.
  
  
• 04:25:00 In this video, the presenter explains how to use a pre-trained TensorFlow model to recognize images of different animals. First, they explain how to load the model into TensorFlow and scale the images to the same size. Next, they show how to apply the model to the training and validation data sets. Finally, they demonstrate how to use the model to recognize images of animals.
  
  
• 04:30:00 In this video, the instructor demonstrates how to create a neural network using TensorFlow 2.0 for training on a dataset of 1000 classes of dogs and cats. The network is pre-trained using a base model and then a prediction layer is added. The instructor also shows how to freeze the trainable parameters of a layer so that the network will not be retrained when using it for prediction. The model is then evaluated and trained on a new dataset.
  
  
• 04:35:00 This video explains how to use TensorFlow to train a model that can predict the class of an image. The video covers basic concepts such as deep learning and Convolutional Neural Networks.
  
  
• 04:40:00 This video explains how recurrent neural networks can be used to understand and process natural language. The first section discusses what natural language processing is and how recurrent neural networks differ from regular neural networks. The second section discusses how to convert textual data into numeric data that can be used by a recurrent neural network. The final section discusses how to use recurrent neural networks to perform sentiment analysis and character slash text generation.
  
  
• 04:45:00 The first method that we will be discussing is called bag of words. This algorithm looks at a sentence and converts it into a numeric representation where every word in the sentence is encoded with a number. This can be useful for tasks where just the presence of a word is enough to determine the meaning of the sentence. However, when dealing with more complex input, this technique can break down.
  
  
• 04:50:00 The video discusses the use of a bag of words encoding for text, as well as a couple of other encoding methods. One problem with this approach is that it loses the ordering of words, which can make it difficult for a machine learning model to correctly identify sentiment in text.
  
  
• 04:55:00 In this video, the instructor explains how word embeddings work and how they can be used to improve the accuracy of a neural network's predictions. The instructor then introduces recurrent neural networks, which are a type of neural network that are especially good at processing textual data.

Part 6

• 05:00:00 A recurrent neural network is a type of neural network that processes data at different time steps and maintains an internal memory. This allows the network to gradually build up an understanding of a text or input.
  
  
• 05:05:00 This video explains the basics of TensorFlow 2.0, which includes a description of a simple recurrent neural network (RNN) layer and a long short-term memory (LSTM) layer. The LSTM layer is designed to help the RNN layer remember the previous sentences in a text sequence, which can become increasingly difficult as the sequence length increases.
  
  
• 05:10:00 The TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial covers the basics of neural networks, including recurrent neural networks, which are used for sentiment analysis on movie reviews.
  
  
• 05:15:00 In this video, the presenter shows how to pad a text review to a specific length using Python, and describes how the model is trained. They also mention that speed up the training process by using a GPU.
  
  
• 05:20:00 In this video, the author provides a complete course on teaching Python neural networks for beginners. The course begins with an introduction to neural networks, followed by a tutorial on how to train a neural network using TensorFlow 2.0. Once the neural network is trained, the author demonstrates how to make predictions on a movie review dataset. The results show that the neural network is not accurate enough, and the author provides a function to encode text into tokens for the neural network to process. The author also provides a decode function to convert text back into words.
  
  
• 05:25:00 This 1-paragraph summary explains how the decode integers function encoded text to a movie review in Python. This function takes a sequence of words and turns them into integers, then uses the ENCODE text function to encode the integers. Finally, the model is used to predict the review for a given piece of text. The first review is predicted to be 72% positive, while the second review is predicted to be 23% positive.
  
  
• 05:30:00 In this final video of the TensorFlow 2.0 Complete Course, a recurrent neural network is created to generate a play based on a Shakespeare text. The file is loaded and read in, and the length of the text is calculated. The first 250 characters are read and analyzed to determine the encoding scheme. Characters are encoded as integers and the vocabulary is created. The index of each character is calculated and stored in the vocabulary.
  
  
• 05:35:00 In this video, the instructor teaches how to use TensorFlow to train a neural network using a text data set. The instructor first converts the text data set into integers, and then trains the neural network using a sequence of 101 training examples per epoch.
  
  
• 05:40:00 This video explains how to create a model in TensorFlow 2.0 using the built-in function "build model." The model is trained on a set of 64 training examples, and then saved so that predictions can be made on one input sequence.
  
  
• 05:45:00 The author describes the steps needed to train a neural network using TensorFlow. First, the author describes the input and output shapes of the neural network. Next, the author explains how to create a loss function to optimize the neural network. Finally, the author demonstrates how to use the model to predict a sequence of 100 samples.
  
  
• 05:50:00 In this video, TensorFlow is explained in detail, including how it works with recurrent neural networks. The video also covers how to create a loss function to determine how well a model is performing.
  
  
• 05:55:00 This video introduces the TensorFlow 2.0 library for training neural networks, and demonstrates how to rebuild a model with a new batch size of one. The video also includes a demonstration of how to generate text with TensorFlow.

Part 7

• 06:00:00 In this video, TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial, the user explains how to generate a sequence of text using a recurrent neural network. The first step is to reset the network's status and generate a list of predictions. Then, the user trains the network on a B movie script, and shows the results.
  
  
• 06:05:00 This video tutorial covers the basics of TensorFlow 2.0, including how to create a simple neural network in Python. Thevideo then moves on to discussing reinforcement learning, which is a more complex, advanced technique. Thevideo concludes with a discussion of terminology and a recap of what reinforcement learning is.
  
  
• 06:10:00 Reinforcement learning is a method of training AI to accomplish tasks in an environment by rewarding them for actions that lead to improved performance. The goal of reinforcement learning is to maximize a reward, which can be anything that the agent is trying to achieve in the environment. In this course, the instructor covers the concepts of state, action, and reward.
  
  
• 06:15:00 The goal of the agent in this example is to maximize its reward in the environment by learning a cue table that predicts the reward for different actions in different states.
  
  
• 06:20:00 TensorFlow is a machine learning library that allows users to create models that can learn from data. In this video, the instructor explains how Q learning works, and how it is used to optimize the behavior of an agent in a simulated environment. He goes on to explain how to use the cue table to optimize the behavior of the agent.
  
  
• 06:25:00 In this video, we learn about the neural networks used in a beginner's tutorial for TensorFlow 2.0. The first part of the video covers the basics of neural networks and how they work. We then explore how to create a simple agent that learns to navigate a 3D environment by either using the current cue table or randomly picking an action. The second part of the video explains the formulas used to update the agent's cue table.
  
  
• 06:30:00 The "TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial" video introduces the TensorFlow 2.0 programming language and its associated data structures and algorithms for neural networks, which are a type of machine learning model. The video then demonstrates how to create and train a simple neural network model using the openAI gym software.
  
  
• 06:35:00 The TensorFlow 2.0 Complete Course starts with introducing the actor-critic model, which is used to train a neural network. It then shows how to create a frozen lake environment using NumPy and TensorFlow. The course covers how to use q learning to solve the navigation problem in the environment.
  
  
• 06:40:00 This video provides a complete tutorial on how to use TensorFlow 2.0 to create a neural network for beginners. First, the instructor explains the epsilon variable and how it affects the chance of the agent taking a random action. Next, the instructor demonstrates how to create a rewards list and how to update the agent's q values using the reward formula. Finally, the instructor shows how to set the agent's current state and check if it has finished exploring the environment.
  
  
• 06:45:00 In this final module of the "TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial," the instructor explains how to use the cue table to adjust the epsilon value to slowly increase the average reward as the agent moves around a maze.
  
  
• 06:50:00 This video tutorial introduces the basics of TensorFlow, a powerful machine learning library. After introducing TensorFlow and its key concepts, the tutorial walks viewers through a series of tutorials on different machine learning tasks such as deep learning and reinforcement learning.

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Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial

Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial

The Keras with TensorFlow course is focused on teaching users how to use Keras, a neural network API written in Python and integrated with TensorFlow. It covers the basics of organizing and pre-processing data, building and training artificial neural networks, and the importance of data normalization and creating validation sets. The course also provides resources such as video and text files and a guide on how to set up a GPU for increased efficiency. Users also learn how to save and load models, including options to save everything, only the architecture or just the weights. The course is suitable for those with basic programming skills and some experience with Python.

The second section of the "Keras with TensorFlow Course" covers a variety of topics, starting with loading weights into a new Keras model with the same architecture as the original model. The instructor then explains how to prepare and preprocess image data for training a convolutional neural network to classify images as either cats or dogs before moving on to building and training a Keras sequential model for the first CNN. The section includes details for training the model using a generator containing label data for validation during model fit, and how to plot a confusion matrix to evaluate model performance. It concludes by demonstrating how to fine-tune a pre-trained VGG 16 model to classify images of cats and dogs, adjust its pre-processing, and train it as well.

In the third section the instructor introduces MobileNets, a smaller and faster alternative to more complex models. They demonstrate downloading and using MobileNets in a Jupyter Notebook, organizing a data set for sign language digits, and fine-tuning the model for a new classification task. The instructor emphasizes the importance of correctly pointing the iterator to the data set's location on disk, the number of layers to freeze during training, and tuning hyperparameters to reduce overfitting issues. The final section introduces data augmentation and its potential to reduce overfitting and increase the dataset's size, and provides instructions on the different types of augmentation (e.g., shifting, flipping, rotating), saving augmented images to disk, and adding them back to the training set.

• 00:00:00 In this section, it is explained that the course is focused on teaching how to use Keras, a neural network API written in Python and integrated with TensorFlow. It begins with the basics of organizing and pre-processing data and then moves on to building and training artificial neural networks. The course recommends some basic programming skills and some experience with Python but will also give brief introductions to each deep learning concept before going through the code implementation. The course also provides video and text resources, including code files used in the course, that are regularly tested and maintained, and download access to these files are available to members of the Deep Lizard Hive Mind. It is further explained that Keras was developed with a focus on enabling fast user experimentation, and being integrated with TensorFlow, Keras is now completely integrated with the TensorFlow API. Recommendations are provided to learning multiple neural network APIs and not sticking with one forever to demonstrate experience and comparison between them, thus making the candidate more valuable.
  
  
• 00:05:00 In this section of the Keras with TensorFlow course, the need for a GPU is discussed, and it is noted that it is not required for the course. However, if a user wants to use a GPU, there is a guide available on how to set up the GPU to work with TensorFlow. It is recommended to go through the course with a CPU first and then set up the GPU for increased efficiency and speed. The next section discusses how to prepare and process numerical data for the artificial neural network and the different formats for data that the sequential model in Keras expects. The fit function expects the input data (x) to be in a NumPy array, a TensorFlow tensor, a dict mapping, a TF data dataset or a Keras generator. The target data (y) also needs to be in the same format as x.
  
  
• 00:10:00 In this section, the instructor explains that data normalization or standardization techniques can put data into a format that makes it easier for the deep learning model to learn from. The instructor uses a simple numerical dataset as an example, where an experimental drug was tested on individuals ranging from age 13 to 100, and around 95% of patients who were in the older population, 65 or older, experienced side effects, while around 95% of patients who were under 65 years old experienced no side effects. The instructor then goes through a for loop that generates random integers that mimic the real-life scenario of patients that either experienced or did not experience side effects, and then adds these samples and corresponding labels to two separate lists.
  
  
• 00:15:00 In this section of the video, the instructor summarizes the process of generating and preparing data for a neural network using NumPy arrays in the Keras API integrated within TensorFlow. They explain that the samples list contains ages, and the labels list contains zeros and ones representing side effects, or no side effects, to correspond with each age. The data is then processed and transformed into the NumPy array format that the fit function expects, and the data is shuffled using the shuffle function to remove any imposed order from the data generation process. Furthermore, the data is rescaled from a scale of 13 to 100, down to a scale of zero to one, and reshaped for the fit transform function to accept one dimensional data. Lastly, the instructor demonstrates how to build an artificial neural network on this data, using a sequential model from the keras API.
  
  
• 00:20:00 In this section of the video, the presenter explains how to create a neural network model using the Keras API integrated with TensorFlow. The model is a sequential model and is created as an instance of the Sequential class. The first dense layer creates the first hidden layer and has 16 units with the ReLU activation function. The second dense layer creates the second hidden layer and has 32 units with the ReLU activation function. The last layer is the output layer with two units representing the two possible output classes. The presenter explains that the output layer is followed by the Softmax function, which gives probabilities for each output class. The presenter then demonstrates how to use the model.summary() function to display a visual summary of the model architecture.
  
  
• 00:25:00 In this section, we learn how to train a neural network on the data that was created and processed in previous sections. After building our model, we compile it with the appropriate loss, optimizer, and metrics. We then specify the input data, target data, batch size, and number of epochs for training in the fit function. Training begins and we see that within 30 epochs, our simple model achieves a 94% accuracy without much effort. This serves as a great example of the ease of getting started with Keras.
  
  
• 00:30:00 In this section, the concept of a validation set is explained in the context of training machine learning models, and the importance of its use is highlighted. The creation of a validation set allows users to gauge how well a model generalizes on data that it has not been trained on. Overfitting can be avoided by examining the accuracy of the validation set results, which should not be significantly lower than that of the training data. Two methods to create and use validation sets with a Keras sequential model are discussed, with the second method allowing Keras to create the validation set for the user.
  
  
• 00:35:00 In this section, the video discusses how to create a validation set from the training set by using the validation split parameter, which splits out a specified percentage of the training data into a validation set. The video notes that the validation set is completely held out of the training set and will be created on the fly whenever the fit function is called. It is also important to shuffle the training data before passing it to fit to ensure that the validation set is not just the last X percent of the non-shuffled data. The video also explains how to use the validation set to check if the model is overfitting or generalizing well, and discusses the next step of using a test set for inference.
  
  
• 00:40:00 In this section, the instructor explains the process of inference in deep learning. Inference is the process of deploying a trained model on the real-world datasets to get the predictions on new data that the model has not seen before. To ensure that the model can generalize well enough to make accurate predictions on new data, the instructor suggests using a test set. The test set should be processed in the same format as the training data. The author demonstrates how to do this by shuffling the test data, scaling it to be between 0 and 1, and predicting the test data with a trained model to obtain the probability of the class to which each element in the test set belongs.
  
  
• 00:45:00 In this section, we look at using a confusion matrix to visually observe how well a neural network model predicts on test data. By importing necessary packages and creating a confusion matrix using scikit-learn, we can compare true labels of the test set with predicted labels, and thus better understand the accuracy of our model's predictions. We also see how to plot the confusion matrix function and how certain values therein are obtained, pre-processed, and visualized. A link to a helpful function for the Keras with TensorFlow course is also available on the deep lizard blog.
  
  
• 00:50:00 In this section, the instructor demonstrates how to plot a confusion matrix to visualize the accuracy of the model's predictions. The plot shows the predicted labels on the x-axis and the true labels on the y-axis. The correct predictions are shown in blue squares going diagonally from the top left to the bottom right of the plot. The confusion matrix allows the user to visualize how well the model is performing and identify the classes that might need improvement. The instructor explains that the confusion matrix is a great tool to use for evaluating a model's performance, and it can help drill down into which classes need further work. Finally, the instructor shows how to save a Keras sequential model using the `.save()` function, which saves the model's architecture, weights, and training configuration to an h5 file.
  
  
• 00:55:00 In this section, the instructor goes over the different ways to save and load a model in Keras with TensorFlow. The first and most comprehensive option is to save and load everything about the model, including its architecture, weights, and training configuration. The second option is to save just the architecture of the model using the "to JSON" function, which can then be used to create a new model with the same architecture at a later time. The third option is to save only the weights of the model using the "save weights" function, which can be loaded into a new model to update its weights, but does not save any other details about the model. The instructor also explains that the same process can be done using YAML strings instead of JSON strings.

• 01:00:00 In this section, the presenter discusses loading weights into a new Keras model with the same architecture as the original model. He explains that the shape of the weights being loaded must match the shape of the model architecture for the mapping of the weights to work. The presenter then shows how to load and populate a new model with weights from the original model using the "load weights" and "get weights" functions in Keras. The section then transitions to a new topic, which is preparing and processing image data for training a convolutional neural network to classify images as either cats or dogs, using the dataset from the Kaggle cats versus dogs competition. The presenter explains the manual and programmatically steps required to prepare the data for processing.
  
  
• 01:05:00 In this section, the instructor organizes the data into three subsets: training, validation, and testing sets. The data set used in the tutorial has 25,000 images of cats and dogs, but to speed up the training process, only 1,000 images are used for training, 200 for validation, and 100 for testing. The data is organized into different directories for each set, and the directory structure is checked to ensure that it does not already exist. The images are selected randomly based on file names, where cat and dog images have the words "cat" and "dog" in their respective file names. Finally, the data path is specified for each set to point to the correct location on disk.
  
  
• 01:10:00 In this section, the video explains how to prepare data for a Keras sequential model by creating batches of data using the image data generator. The training, validation, and testing sets are defined and resized to a specified height and width for uniformity. The pre-processing function, tf.keras.applications.VGG16.preprocess_input, is applied to the images before they're passed to the network. The video warns viewers not to stress over the technical details of pre-processing, as it will be explained in future episodes. Additionally, the video specifies that shuffle equals false for the test set, explaining that when running inference, the unsettled labels for the test set are needed to view prediction results in a confusion matrix, and the data must not be shuffled to access them.
  
  
• 01:15:00 In this section, the instructor demonstrates how to obtain and organize image data for a convolutional neural network. The train batches, consisting of ten images and corresponding labels, are plotted using a pre-processing function that skews the RGB color data. However, the images can still be distinguished as either a cat or dog with the help of one hot encoded vectors for the labels. The instructor notes that sometimes corresponding labels for the test set may not be available, and directs viewers to the blog for guidance on handling such cases. The episode concludes with a preview of the next section where a convolutional neural network will be built and trained on cat and dog image data.
  
  
• 01:20:00 In this section of the TensorFlow tutorial, a Keras sequential model is used for the first convolutional neural network (CNN). The first layer of the model is a 2D convolutional layer with 32 filters and a kernel size of 3x3 followed by the popular ReLU activation function with "same" padding to zero pad the images. The input shape is specified as 224x224x3 for the RGB-format images. The first convolutional layer is then followed by a max pooling layer with a pool size of 2x2 and strides of 2. Another 2D convolutional layer, similar to the first one but with 64 filters instead of 32, is added followed by another max pooling layer. The flattened output of the max pooling layer is then passed to a dense output layer with two nodes corresponding to cat and dog, respectively. The output layer is followed by the softmax activation function to give probabilities for each corresponding output from the model.
  
  
• 01:25:00 In this section, the instructor discusses how to train a model using Keras with TensorFlow and how to use a generator containing label data for validation during model fit. The model is trained with compile, and then the fit function is defined using the training and validation sets, and the epoch and verbosity are set. The warning that occurs during training is a bug within TensorFlow, and the instructor points out how to ignore this warning. The results show that while the accuracy on the training set is 100%, the validation accuracy is only 69%, indicating overfitting. The model will need further attention to combat the overfitting problem if it is to be used in production. The next episode will explore how the trained model holds up to inference in predicting on images in the test set.
  
  
• 01:30:00 In this section of the video, the instructor explains the process of using a plot images function to plot a batch of test data from test batches and printing the corresponding labels for the images. The instructor emphasizes the importance of not shuffling the test data set to ensure the correct mapping between labels and samples. Next, the predictions are obtained by calling model.predict with test batches specified as the input. The instructor prints out the rounded predictions and explains how to interpret them. They also mention the use of a confusion matrix to visualize the results, and the confusion matrix function from scikit-learn is used with the true labels passed in using test batches classes.
  
  
• 01:35:00 In this section, the video discusses how to plot a confusion matrix to evaluate the performance of the model in classifying images of cats and dogs using TensorFlow's Keras API. The confusion matrix is plotted using a function from scikit-learn and the class indices are adjusted accordingly. The diagonal of the confusion matrix represents the correct predictions, and the model appears to be overfitting. The next section will demonstrate how to fine-tune a pre-trained VGG 16 model to classify images of cats and dogs, which won the 2014 ImageNet competition. The video also briefly explains the VGG 16 pre-processing function, which only subtracts the mean RGB value computed from each pixel of the training set from the image data.
  
  
• 01:40:00 In this section, the instructor explains the pre-processing that was done for the VGG-16 model and how the new data needs to be processed in the same way to match how VGG-16 was originally trained. The instructor mentions that Keras has functions built-in for popular models like VGG-16, which has pre-processing in place that matches for the corresponding model. The instructor also explains that the VGG-16 model originally was predicting for 1000 different imageNet classes, and the objective is to change the last output layer to predict only two output classes corresponding to cat and dog. Finally, the instructor creates a new sequential model by looping through every VGG-16 layer and excluding the last output layer. This new model is for fine-tuning and only has two output classes.
  
  
• 01:45:00 In this section, we see how to modify and train the fine-tuned VGG 16 model on a dataset of cats and dogs. The last layer in the model, which predicts 1000 output classes, has been removed, and a new dense layer that has only two output classes for cat and dog is added. All the previous layers have been set to be not trainable, except for the output layer containing 8000 trainable parameters. The model is compiled using categorical cross-entropy as the loss and accuracy as the metric, and it is trained using the fit() method by passing the training dataset and validation set to it.
  
  
• 01:50:00 In this section, the instructor discusses the results of training the VGG 16 model on images of cats and dogs with a specificity on the accuracy of the training and validation sets. The VGG 16 model had already been trained on images of cats and dogs from the image net library, and the training that's being done on the output layer is to train the model to output only cat or dog classes. In just five epochs, the VGG 16 model had a training accuracy of 99%, while the validation accuracy was on par at 98%, indicating how well this model generalizes to cat and dog data in validation sets compared to the simple convolutional neural network. In the next episode, the fine-tuned VGG 16 model will be used for inference to predict on cat and dog images in the test set, and given the accuracy on the validation set, we should expect to see good results on the test set too.
  
  
• 01:55:00 In this section of the video, the instructor discusses the use of a confusion matrix to evaluate the performance of a fine-tuned VGG16 model on an unseen test set. The diagonal of the confusion matrix is examined, which reveals that the model achieved an accuracy rate of 96%. While the fine-tuning approach taken for the VGG16 model was minimal, the instructor explains that upcoming episodes will cover more fine-tuning methods on other pre-trained models, such as MobileNets. These smaller, low power models are better suited for deployment on mobile devices due to their considerably smaller size and number of parameters.

• 02:00:00 In this section of the video, the instructor introduces MobileNets - a smaller and faster alternative to bigger models like VGG 16. While MobileNets are not as accurate as some of these heavy models, the reduction in accuracy is relatively small. The instructor walks through the steps to download and work with MobileNets in a Jupyter Notebook, including importing the necessary packages, downloading and assigning the MobileNet model, and creating a function called "prepare_image" to resize and format the images before passing them through the MobileNet model for processing. Overall, the focus is on understanding the tradeoffs between accuracy and size/speed when working with different deep learning models and how to use them effectively.
  
  
• 02:05:00 In this section, the instructor demonstrates how to use a pre-trained MobileNet model to predict the top five possible classes of given images. They first display a lizard image and pass it through a pre-processing function before passing it to MobileNet's predict function. MobileNet predicts the top three classes with high probabilities, with American chameleon being the most probable. They repeat the same process with an image of a cup of coffee and the model predicts that it's an espresso with a 99% probability. Finally, they pass a strawberry image to the model and get the predictions for the top possible classes.
  
  
• 02:10:00 In this section of the video, the presenter shows the results of the mobile net model's predictions for a random sample of images, which includes a strawberry and other fruits. The presenter mentions that although there was a small reduction in accuracy for the model, it is not noticeable when doing tests like the ones shown in the video. The next step is to fine-tune the mobile net model for a custom data set consisting of sign language digits. The presenter shows how to organize the data set on disk and move it to the working directory before processing it programmatically in a Jupyter Notebook.
  
  
• 02:15:00 In this section, the instructor explains how to organize image data into train, valid, and test directories using a Python script. The first step is to check the number of samples in each class, which ranges from 204 to 208. Then, using a for loop, the script moves each class directory to the train directory and creates separate directories for valid and test sets for each class. Finally, the script samples random images from each class in the train directory, moves them to the valid directory, and samples more images and moves them to the test directory. After running the script, the data is organized into separate directories for each set, and the number of images in each class can be verified by checking the corresponding directories.
  
  
• 02:20:00 In this section of the video, the speaker discusses the organizational structure of the data set they will be working with, which is similar to the cat and dog data set used earlier in the course but now contains 10 classes instead of two. They then demonstrate how to preprocess the data by defining the train, validation, and test directories and creating directory iterators using the image data generator. The pre-processing function used is the mobile net pre-processing function, which scales the image data to be on a scale from minus one to one. The iterator settings, such as the target image size and batch size, are also defined. The speaker emphasizes the importance of correctly pointing the iterator to the data set's location on disk, as incorrect pointing could result in zero images being found.
  
  
• 02:25:00 In this section of the Keras with TensorFlow course, the instructor explains how to fine-tune the pre-trained MobileNet model for a new classification task. The first step is to download the MobileNet model and examine its layers using the "model.summary()" function. They then select the layers up to the sixth to last layer and create a new dense output layer with 10 units. This is followed by creating a new functional model that passes all previous layers up to the sixth to last layer and mobile net to the output layer. The new model is created and the next step is to freeze all but the last 23 layers for training. The instructor notes that the number of layers to freeze requires personal experimentation and can vary depending on the task.
  
  
• 02:30:00 In this section, the instructor fine-tunes the original MobileNet model, training only the last 23 layers and replacing the output layer with one that has 10 classes instead of 1,000. The model is compiled and trained using the Adam optimizer with a 0.0001 learning rate and categorical cross-entropy loss function. After training for 30 epochs, the model achieves 100% accuracy on the training set and 92% accuracy on the validation set. Although there is some overfitting, with validation accuracy lower than training accuracy, the instructor suggests that running more epochs or tuning hyperparameters may help to reduce the overfitting issue.
  
  
• 02:35:00 In this section of the video, the instructor shows how to use a fine-tuned model on the test set and plot the predictions to a confusion matrix. The confusion matrix indicates that the model has performed well with mostly correct predictions on the test set. The model has 90% accuracy on the test set, which is expected given the accuracy on the validation set. The instructor emphasizes that the series on MobileNet has provided insight into how to fine-tune models for custom datasets and use transfer learning. The next episode will demonstrate how to use data augmentation on images using TensorFlow's Keras API.
  
  
• 02:40:00 In this section, the importance of data augmentation for deep learning is highlighted, especially when there is not enough data to train the model on. Data augmentation can help to reduce overfitting and increase the size of the training set. The code for augmenting image data using Keras is then introduced, where an image data generator is created and various augmentation parameters are specified, such as rotation range, width shift range, and zoom range. A random image from a dog directory is chosen and the flow function is called to generate a batch of augmented images from this single image. The resulting augmented images are then plotted using a pre-defined plot images function.
  
  
• 02:45:00 In this section, the instructor discusses data augmentation, which is a method of artificially increasing the size of a dataset by creating variations of existing data. By looking at different images, the instructor identifies the types of data augmentation that have been done to them, such as shifting, flipping, and rotating, and explains how this technique can be helpful for growing a dataset. By increasing the variety of the dataset, a model can become more robust and better at classifying data. The instructor also provides a brief instruction for saving these augmented images to disk and adding them back to the training set.

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Scikit-learn Crash Course - Machine Learning Library for Python

Scikit-learn Crash Course - Machine Learning Library for Python

The "Scikit-learn Crash Course" video provides an overview of using the Scikit-learn library for machine learning in Python. The video covers data preparation, model creation and fitting, hyperparameter tuning through grid search, and model evaluation. The importance of pre-processing and transformers in enhancing model performance is emphasized, with examples of standard scaler and quantile transformer. The video also discusses the significance of model evaluation and choosing the right metric for the problem, as well as handling imbalanced datasets and unknown categories in one-hot encoding. The speaker emphasizes understanding the data set and potential biases in model predictions, and provides an example of credit card fraud detection.

The second part pf the video covers several topics, including grid search, metrics, pipelines, threshold tuning, time series modeling, and outlier handling. The instructor explores the use of custom-defined metrics and the importance of balancing precision and recall in model creation. Additionally, the voting classifier is showcased as a meta-estimator that increases model flexibility and expressiveness. The video concludes by introducing the Human Learn tool, which helps construct and benchmark rule-based systems that can be combined with machine learning algorithms. Furthermore, the FunctionClassifier tool is explored, which allows users to create customized logic as a classifier model and add behaviors such as outlier detection. Overall, the video provides a comprehensive overview of Scikit-learn and its flexible API, emphasizing the importance of understanding the relevant metrics for model creation and customization.

• 00:00:00 The data y. X contains the features or input variables, while Y contains the target or output variable that we want to predict. We split the data into a training set and a test set to evaluate the performance of the model. Next, we need to preprocess the data. One of the most important preprocessing techniques is scaling, which involves normalizing the values of the features so that they all fall within a similar range. This helps the model to learn more effectively. Finally, we choose a model and train it on the training set. Once the model is trained, we evaluate its performance on the test set and make predictions on new data. In the next sections, we will dive deeper into the topics of preprocessing, model evaluation, and meta estimators.
  
  
• 00:05:00 In this section, the speaker discusses how to split data into x and y sets for machine learning models. The x set represents the data used to make predictions, while the y set contains the prediction of interest. Using the scikit-learn library, users can load benchmark datasets for educational purposes. The speaker also explains the two-phase process of creating and fitting a model to learn from the data in order to make predictions. The k-nearest neighbor model is used as an example, but the Linear Regression model is also shown to illustrate how different models can still have the same API within scikit-learn.
  
  
• 00:10:00 In this section, the Scikit-learn Crash Course video explains how the K-nearest neighbor model works in a simple dataset that features the square feet that a house has and its proximity to schools. The model makes predictions based on the nearest 5 neighbors, but the challenge may arise when using distances that have different scales, meaning one axis may have a much bigger effect on the predictions than another. This requires rethinking what a machine learning model is, and suggests that there needs to be some preprocessing before the data is given to the K-nearest neighbor model, so that the scaling is done before predictions are made, and that everything inside the pre-processing box is part of the model.
  
  
• 00:15:00 In this section, the video explores the concept of a pipeline in Scikit-learn, which allows the chaining of processing steps and the calling of dot fit and dot predict for the entire pipeline when trained. By using a pipeline, it is possible to handle pre-processing steps automatically, such as scaling and normalization, to learn from the data to ensure a robust system. However, this approach introduces an issue where the model is allowed to memorize the original data. The video showcases how the chart falsely suggests a perfect prediction, but the model can only do so because it's allowed to memorize the original data point, preventing a fair comparison and reliable predictability on new data.
  
  
• 00:20:00 In this section, the instructor explains the method for evaluating a model's performance using a separate testing dataset to prevent judging the model on the same data set it was trained on. He suggests dividing the dataset into segments and using one for training and the others for testing. Additionally, he introduces Scikit-learn's grid search cv object, which allows automated cross-validation to determine the best hyperparameters to use in the model. This object can be used with the pipeline created earlier to fine-tune the model and improve its predictive capabilities.
  
  
• 00:25:00 In this section, the instructor goes through various parameters of the KNearestNeighbors model and demonstrates how to use the GridSearch object to find the best set of hyperparameters. The GridSearch object will automatically perform cross-validation and keep track of the results, making it easy to analyze and choose the best configuration. However, the instructor highlights that while using the appropriate API and building blocks is important when working with scikit-learn, it is equally important to understand and analyze the data set being used. The instructor shows how to access and display the description of a data set and emphasizes the importance of taking the time to understand the variables in the data set before building a model.
  
  
• 00:30:00 In this section, the speaker discusses the potential pitfalls of blindly applying machine learning models without fully understanding the data being used for training and prediction. Even seemingly innocuous variables, such as the proportion of a certain race in a town, can lead to biased and even racist algorithms. The speaker emphasizes the importance of thoroughly examining and testing the model before deploying it in production. The use of grid search and other methods can provide statistical guarantees, but it can also create a false sense of optimism and cause blind spots. It's the responsibility of the user to educate themselves on the data being used and to consider ethical and feedback mechanisms, as well as fallback scenarios for when things go wrong in production.
  
  
• 00:35:00 In this section, the importance of data transformation in model building is highlighted. The video discusses the use of transformers and their significance in enhancing model performance. The standard scaler from Scikit-Learn is used to rescale a dataset, and while its performance is good, the video demonstrates how it could be improved by using other scaling methods. The approach involves subtracting the mean from the data set and dividing by the standard deviation. While the approach scales the data, it leaves outliers, which might affect some algorithms. The video further emphasizes the importance of pre-processing in achieving the desired model outcomes.
  
  
• 00:40:00 In this section of the video, the speaker discusses the concept of normalization and how to use it effectively by calculating quantiles instead of mean values. By using quantiles, the effect of outliers on the data is reduced and the process becomes more robust. The speaker demonstrates how to implement the quantile transformer as a pre-processing step, replacing the standard scaler, to achieve better results in machine learning models. The profound effect of the transformer on the output data is shown through a plot output function that trains a k-nearest neighbor model and produces predictions for both the standard scaler and the quantile transformer.
  
  
• 00:45:00 In this section of the video, the speaker explains how preprocessing can drastically affect the classification of data sets. In the first example, the speaker employs the Quantile Transformer which makes the model more stable and better suited in dealing with outliers in the long run. The second example shows how adding new features can improve the model's performance. By generating non-linear features like x1 times x2 and x1 to the power of 2 and x2 to the power of 2 through Polynomial Features, the Logistic Regression Model was able to produce a near-perfect classification. Finally, the One Hot Encoder is introduced as a technique for transforming text or categories into numeric data that is useful for predicting classes like low, medium or high risk.
  
  
• 00:50:00 In this section, the video discusses how to handle unknown categories when using one-hot encoding with the Scikit-learn library. The presenter explains that if the encoder is asked to transform data that it has not seen before, it will result in a value error. However, this can be changed by adjusting the "handle_unknown" parameter to "ignore". The video also introduces a website called "DrawData.xyz", which allows users to create their own data sets for practice in pre-processing and pipelines. The speaker emphasizes that pre-processing steps are crucial because they can strongly impact the outcome of the model. Finally, the video explains the benefits of using a grid search to compare multiple models and to choose the best one for prediction, using a credit card fraud data set as an example.
  
  
• 00:55:00 In this section, the video explains the importance of choosing the right metric and how Scikit-learn handles it. The video uses a dataset of anonymized features and transaction amounts to demonstrate how Scikit-learn can be used to predict fraud cases accurately. However, since the dataset is imbalanced with more fraud cases than non-fraud cases, the model fails to converge with the default number of iterations. Therefore, the maximum number of iterations is increased, and the class weight parameter is adjusted to double the weight of fraud cases to improve the model's fraud detection. The video also introduces GridSearchCV to find the best value for the class weight parameter.

• 01:00:00 In this section, the instructor explains how to use grid search to find the best model parameters for logistic regression in scikit-learn. By defining a grid of parameter values, the instructor shows how to loop through these hyper-parameters and test them using cross-validation. The grid object returns a dictionary of results, including the class weight and scores. The logistic regression model has a bound method called score, which is used to determine the best model. The instructor then introduces two metrics from the scikit-learn metrics module, precision and recall score, and explains how to include them in the grid search using a scoring dictionary.
  
  
• 01:05:00 In this section, the speaker adds the test precision and test recall scores and sets a flag that will enable the addition of train scores to the cross-validation results. They then increase the number of cross-validations and replace the range of four with a numpy linear space, setting a higher value to tell the algorithm to focus on fraud cases. They make some charts summarizing the results, noting that focusing on recall or precision results in a completely different outcome. They then decide to create their metric, the min recall precision, which calculates the minimum between precision and recall, and adds it to the grid search. They do this to balance the two metrics and have a model that balances the recall and precision scores.
  
  
• 01:10:00 , we're using the make_scorer function in the grid search with our custom defined metric, min_precision_recall, which accepts y_true and y_pred as input. However, the make_scorer function requires an estimator, X dataset, y true, and some form of sample weight, so it turns our metric function into a predict scorer object that can be used in the grid search. The sample weight is an extra feature that can be passed along to machine learning models and allows us to say that a particular row is more important than another. Instead of using the make_scorer function, we can directly pass the estimator and calculate the predicted y values with the predict function.
  
  
• 01:15:00 In this section, the speaker discusses the use of sample weight in detecting fraud and shows how it can be added to a function for numerical stability. They also demonstrate the effect of adding sample weight on the algorithmic model. The speaker then proposes using an outlier detection algorithm to detect fraud, and shows how to adapt the metrics for this approach. They replace the logistic regression with an isolation forest algorithm and demonstrate how this affects the precision and recall scores. Finally, the speaker writes their variant of the metrics functions to turn the outlier prediction into fraud label predictions.
  
  
• 01:20:00 In this section, the narrator discusses the flexibility of Scikit-learn's API and the ability to use outlier detection algorithms as if they were classifiers by passing in y labels in custom metrics. This can be useful in judging if an outlier model would be useful in a classification problem. However, the narrator cautions that it is important to concern ourselves with the quality of the labels as this can significantly impact the model's metrics. Additionally, the narrator points out some customization settings for metrics such as specifying whether greater is better and the need for a probability measure for some metrics. Lastly, the narrator mentions the capability of Scikit-learn to work with meta models.
  
  
• 01:25:00 In this section, the instructor explains how scikit-learn pipelines work and the limitations of using only scikit-learn pipelines for machine learning models. The instructor suggests using meta-estimators as a way around the limitations of pipelines to add extra steps and post-processing tools to a model. The instructor provides an example of a meta-estimator, the voting classifier, and how it can be used to balance different models for a dataset by giving weights to each estimator.
  
  
• 01:30:00 In this section, the instructor demonstrates the use of a voting classifier with scikit-learn, which can combine different models that work in different ways to produce more accurate predictions. Moreover, this model can be used as input by other estimators, thus increasing flexibility and expressiveness. The instructor also introduces the idea of tuning the threshold value in a logistic regression model to trade-off precision for recall or vice versa using the thresholder meta-model. This is demonstrated by an example of how to use the thresholder model in a grid search. By using these techniques provided by scikit-learn, one can adjust model parameters and get better performance.
  
  
• 01:35:00 In this section, the speaker talks about using a grid search to tune the threshold of a binary classifier. They show that the precision and recall curves change as the threshold changes, but the accuracy stays relatively consistent. The speaker explains that this kind of post-processing step is best implemented as a meta-model that accepts another model as input. The speaker then demonstrates an example of building a meta-model with Scikit-learn's Pipeline and Group( byRegressor classes to group data by diet and then use a different linear regression model for each group. They explain that this kind of grouping can be useful for situations where the effect of a categorical variable might not simply be a constant shift.
  
  
• 01:40:00 In this section, the video explains a time series task and shows how to model it using a grouped predictor and a dummy regressor. It notes that the prediction is good for the middle year but undershoots in the recent years and overshoots in the past. To make the model better, the video suggests focusing on the more recent data and forgetting the past data. It introduces the concept of sample weights, which allows the user to specify how much to weigh a particular data point. The video then demonstrates how to add decay to the model, which makes the recent data points matter more than the old ones. By doing this, the model becomes more expressive and performs better on the recent data while ignoring the far-away history. However, this trick can result in a situation where the training data has worse performance than testing data.
  
  
• 01:45:00 In this section, the speaker introduces a tool called Human Learn, an open-source project designed to offer scikit-learn compatible tools that help construct and benchmark rule-based systems. The speaker explains that, in the past, it was more common to use human experts to come up with business rules and systems for data classification rather than machine learning algorithms. However, machine learning models are not perfect and may exhibit behavior that is problematic or ineffective. The goal of Human Learn is to construct rule-based systems that can be easily benchmarked against machine learning models while also being combined with them. The speaker demonstrates how to construct a rule-based system using the Titanic dataset, and explains how Human Learn can make incorporating these systems easier in your daily workflow.
  
  
• 01:50:00 In this section of the video, the speaker introduces the FunctionClassifier tool in scikit-learn that takes a user-defined function and turns it into a scikit-learn compatible classifier. The tool also allows for grid search optimization of any parameters defined in the user's function. The speaker demonstrates how to use the FunctionClassifier tool to conduct a grid search on a threshold parameter with a dataset related to the Titanic disaster. The tool's flexibility allows users to create any Python function with customized logic as a classifier model, and add behaviors such as outlier detection.
  
  
• 01:55:00 In this section, the instructor explains how outliers can be handled in pre-existing machine learning models. By adding a logic in front of the model, an outlier can be identified and assigned a different label. The package allows rule-based systems to be constructed using pre-existing machine learning models, striking a balance between natural intelligence and artificial intelligence. The instructor uses the scikit-lego package and the load penguins function to illustrate the efficient prediction of the species of a penguin based on its properties. The function uses an interactive chart, and the instructor draws polygons around the data points that require classifying. The point-in-poly algorithm is then used to classify the data points. An interactive classifier enables the definition of a scikit-learn compatible model from the json blob. Then, x and y datasets can be generated from a data frame, and the model can be used like any cycler model.

• 02:00:00 In this section, the speaker emphasizes the properties and benefits of drawn machine learning models. By using a chart feature in Matplotlib, he demonstrates how the drawn models predict classifications for new examples and handle missing data effectively. Furthermore, the speaker shows how the same drawing can be used as an outlier detection system by checking whether points are outside of polygons. He also demonstrates how the drawing mechanic can be used to assign labels to data points, even when labels aren't readily available, making it a useful pre-processing step.
  
  
• 02:05:00 In this section, the speaker discusses the Interactive Charts API, which is relatively experimental and can act as either a scikit-learn transformer or in a pandas pipeline to add two new columns with counts concerning the data point's appearance in a polygon. The speaker recommends using machine learning algorithms alongside business rules to create rule-based systems. Furthermore, the speaker suggests several resources such as freeCodeCamp, pi data YouTube channel, and the scikit-learn documentation page to learn more about machine learning and scikit-learn.

[MetaQuotes]

PyTorch for Deep Learning & Machine Learning – Full Course (parts 1-4)

PyTorch for Deep Learning & Machine Learning – Full Course

00:00:00 - 01:00:00 The "PyTorch for Deep Learning & Machine Learning" online course instructor Daniel Bourke introduces viewers to the course, which focuses on implementing machine learning concepts in PyTorch, using Python code. Key topics covered in the course include transfer learning, model deployment, and experiment tracking. The video provides an introduction to machine learning and deep learning, and their differences, with deep learning being better for complex problems that require large amounts of data, and providing insights in unstructured data. The anatomy of a neural network is explained, and the course covers the different paradigms of machine learning, such as supervised learning and transfer learning. Additionally, the video explores the potential applications of deep learning, particularly in object detection and natural language processing. Finally, the benefits of PyTorch are explained, such as standardizing research methodologies and enabling the running of machine learning code on GPUs for efficient mining of numerical calculations.

01:00:00 - 02:00:00 This part covers the basics of PyTorch, pre-processing data, building and using pre-trained deep learning models, fitting a model to a dataset, making predictions, and evaluating the model's predictions. The instructor emphasizes the importance of experimentation, visualization, and asking questions, as well as using the course's resources, including GitHub, discussions, and learnpytorch.io. Learners are also introduced to Google Colab, which provides the ability to use GPU or TPU acceleration for faster compute time, pre-installed PyTorch, and other data science packages. The course goes in-depth about tensors as the fundamental building blocks of deep learning, demonstrating how to create tensors with different dimensions and shapes, including scalar, vector, and matrix tensors. The course also covers creating random tensors, tensors of zeros and ones, and how to specify data types, devices, and requires grad parameters when creating tensors.

02:00:00 - 03:00:00 In this PyTorch tutorial, the instructor covers various aspects of tensor operations, including troubleshooting, manipulation, matrix multiplication, transposing, and aggregation. They explain the importance of maintaining the correct tensor shape and data type when working with deep learning models and demonstrate how to check and change these parameters using PyTorch commands. The tutorial includes challenges for viewers, such as practicing matrix multiplication and finding the positional min and max of tensors, and provides useful tips for avoiding common shape errors and improving performance, such as using vectorization over for loops. Additionally, the instructor introduces several helpful PyTorch functions for reshaping, stacking, squeezing, and unsqueezing tensors.

03:00:00 - 04:00:00 This part covers various topics related to PyTorch, including tensor manipulation methods such as reshape, view, stacking, squeeze, unsqueeze, and permute. The instructor provides code examples, emphasizes the importance of tensor shape manipulation in machine learning and deep learning, and challenges viewers to try indexing tensors to return specific values. The course also covers converting data between PyTorch tensors and NumPy arrays and the default data types of each, as well as the concept of reproducibility in neural networks and the use of random seeds to reduce randomness in experiments. The instructor explains how to access GPUs for faster computations and provides options such as Google Colab, Colab Pro, using your own GPU, or using cloud computing services like GCP, AWS, or Azure.

04:00:00 - 05:00:00 This part covers a wide range of topics for beginners, including how to set up GPU access with PyTorch, using the nn module in PyTorch, creating linear regression models, and more. The instructor emphasizes the importance of device agnostic code to run on different devices and to keep in mind the type of device that tensors and models are stored on. The course also includes exercises and extra curriculum to practice what has been learned, and the instructor provides tips on how to approach the exercises in Colab. The course covers training and evaluating machine learning models, splitting data into training and test sets for generalization, and visualizing data. The instructor explains how to create a linear regression model using pure PyTorch, which involves creating a constructor with the init function, creating a weights parameter using nn.parameter, and setting it to random parameters using torch.rand.

05:00:00 - 06:00:00 This part covers topics such as creating a linear regression model using PyTorch, implementing optimization algorithms like gradient descent and backpropagation through PyTorch, and understanding how to test a PyTorch model's predictive power. The importance of using the torch.inference_mode context manager when making predictions, initializing model parameters, using loss functions to measure the accuracy of a model's predictions, and optimizing model parameters to improve the model's accuracy are also discussed. Additionally, fundamental modules in PyTorch, including torch.nn, torch.nn.module, torch.optim, and torch.utils.dataset, are presented.

06:00:00 - 07:00:00 This part covers various aspects of PyTorch and machine learning. One section focused on the steps needed to build a training loop in PyTorch, including looping through the data, computing loss, and performing back propagation. The instructor emphasized the importance of choosing the appropriate loss function and optimizer and introduced the concept of gradient descent. Another section discussed the optimizer and learning rate, and how they impact the model's parameters. The video also emphasized the importance of testing and provided an overview of creating test predictions and calculating test loss. The course provides additional resources for those interested in the mathematical background of backpropagation and gradient descent.

07:00:00 - 08:00:00 This part covers multiple topics related to PyTorch. The course discusses the importance of tracking the progress of a model by keeping a record of the loss values and plotting the loss curves, which should show a decreasing trend. The instructor also explains the methods of saving and loading PyTorch models, which include saving a state dictionary, loading the model using the torch.nn.module.loadStateDict method or the torch.load method, and testing the loaded model. In later sections, the course covers creating linear regression models and using pre-existing models in PyTorch, such as the linear layer, by subclassing nn.module.

08:00:00 - 09:00:00 The part covers a wide range of topics in the realm of deep learning and machine learning. The first section covers the different layers available in torch.nn, pre-built implementations of these layers, and how to train PyTorch models using loss and optimizer functions. In subsequent sections, the instructor explains the importance of device agnostic code, saving and loading PyTorch models, and how to approach classification problems. The instructor provides examples and emphasizes the importance of numerical encoding for inputs, creating custom data, and the design complexities involved in a classification model such as the number of hidden layers, neurons, loss function, and optimizer. Finally, the instructor emphasizes that starting any machine learning problem with data is the most important step.

09:00:00 - 10:00:00 This part provides an overview of how to create and train a neural network using PyTorch for binary classification. The video covers a wide range of topics, including creating a custom dataset, checking input and output shapes, preparing data for training, creating and sending a model to a GPU, selecting an optimizer and loss function for a model, and making predictions. The course includes practical demonstrations of these concepts and aims to provide a comprehensive understanding of using PyTorch for machine learning projects.

10:00:00 - 11:00:00 This part covers several topics, including loss functions, optimizers, activation functions, training loop, and evaluation metrics. The instructor explains how to set up the loss function and optimizer, create an accuracy function, and convert raw logits to prediction probabilities and labels. The course also reviews the difference between BCE loss and BCE with logits loss, and how to calculate test loss and accuracy for a classification model. Additionally, the instructor provides tips on improving a model's performance, such as increasing the depth of the neural network, adjusting hyperparameters, and importing and using helper functions from external Python scripts.

11:00:00 - 12:00:00 In this part the instructor explains how to improve a model by changing hyperparameters such as the number of hidden units, the number of layers, and the number of epochs, and highlights the importance of testing changes one at a time to identify improvements or degradations. They also discuss the differences between parameters and hyperparameters and why it's important to make this distinction. Additionally, the instructor covers troubleshooting techniques when a model is not working and introduces the importance of nonlinearity in machine learning and deep learning models. The instructor demonstrates these concepts with various examples, including linear and nonlinear regression problems, and shows how to train and evaluate models while testing different hyperparameters and loss functions.

12:00:00 - 13:00:00 This PyTorch for Deep Learning and Machine Learning course covers basic to advanced PyTorch concepts for building models. The instructor introduces the concept of nonlinearity and demonstrates how to build classification models using nonlinearity with PyTorch. They also discuss building optimizers, loss functions, and custom activation functions. The importance of combining linear and nonlinear functions to find patterns in data by stacking layers of these functions to create a model is emphasized. The course covers both binary and multi-class classification models and explains how to set them up in PyTorch. The section concludes by demonstrating how to initialize multi-class classification models with input features and output features.

13:00:00 - 14:00:00 The instructor in this part discusses creating a linear layer stack model using PyTorch's nn.Sequential method to perform multi-class classification. They explain the creation of the loss function and optimizer using cross-entropy loss and stochastic gradient descent (SGD). The instructor also discusses dropout layers and the importance of troubleshooting machine learning code to resolve errors. They demonstrate the evaluation of the trained model using various classification evaluation methods such as accuracy, precision, recall, F1 score, confusion matrix, and classification report using torchmetrics and scikit-learn libraries. Finally, the instructor shows how to import and use pre-built metrics functions in PyTorch using the torchmetrics package and provides links to the torchmetrics module and extracurricular articles for further exploration.

14:00:00 - 15:00:00 This part covers various topics related to PyTorch and computer vision using machine learning. This includes understanding computer vision problems such as binary or multi-class classification problems, and learning how a machine learning model learns patterns from various examples of images. The video also explains PyTorch libraries, such as TorchVision, and how it contains datasets, pre-trained models, and transforms for manipulating vision data into numbers usable by machine learning models. In addition, the instructor covers the input and output shapes of the FashionMNIST dataset, the importance of visualizing and exploring datasets to identify potential issues, and provides demonstrations on how to plot and visualize image data using PyTorch and Matplotlib.

15:00:00 - 16:00:00 This video course on PyTorch for Deep Learning and Machine Learning covers the importance of preparing data and using PyTorch data sets and data loaders. The concept of mini-batches in deep learning is emphasized, and the process of creating train and test data loaders is explained using PyTorch, with the batch size hyperparameter set to 32. The importance of visualizing images in a batch is discussed, and the concept of flattening is introduced to transform multi-dimensional data into a single vector for use in a PyTorch model. The process of creating a simple neural network model with a flatten layer and two linear layers is covered, and the concept of using helper functions in Python machine learning projects is explained. Finally, the importance of timing functions for measuring how long a model takes to train and the use of TQDM for a progress bar is demonstrated.

16:00:00 - 17:00:00 This part of the course covers various topics related to PyTorch, starting with setting up the training and testing loops, troubleshooting common errors, evaluating models, and making predictions. The instructor emphasizes the importance of experimentation to find the best neural network model for a given dataset and discusses the benefits of nonlinearity for modeling nonlinear data. They also demonstrate how to create helper functions in PyTorch, optimize and evaluate loops, and perform training and testing steps. The course further explores device-agnostic code and the advantages of training models on CPUs and GPUs, concluding with a demonstration of how to measure training time on both devices.

17:00:00 - 18:00:00 This part covers many topics in deep learning and machine learning. The instructor demonstrates how to create and test a deep learning model, build a convolutional neural network (CNN) using PyTorch, and create blocks in PyTorch. Additionally, the tutorial goes over the composition of a PyTorch model and how convolutions work in practice, changes to stride and padding values in a convolutional layer, and the convolutional and max pooling layers in PyTorch. Throughout the video, the instructor shares resources, provides PyTorch code and step-by-step explanations, and offers guidance on how to create efficient and reusable code.

19:00:00 - 20:00:00 This part covers various topics such as visualizing machine learning model predictions, evaluating a multi-class classification model using confusion matrix in PyTorch, installing and upgrading packages in Google Colab, saving and loading a PyTorch model, and working with custom datasets. The course also demonstrates the process of building a computer vision model using PyTorch. The instructor emphasizes the importance of utilizing domain libraries for data loading functions and customizable data loading functions and provides examples for various categories such as vision, text, audio, and recommendation. They also provide helpful resources such as the learn pytorch.io website and the PyTorch deep learning repo.

20:00:00 - 21:00:00 The instructor of this PyTorch for Deep Learning & Machine Learning course starts by introducing the Food 101 dataset, but provides a smaller subset with three food categories and only 10% of the images as practicing with PyTorch. The instructor emphasizes the importance of having a separate directory for data and then shows how to open, visualize, and transform images using the Python image library Pillow and PyTorch methods. The section also covers data transformations with PyTorch, such as resizing and flipping images, and the instructor demonstrates how to load and transform images as tensors for machine learning models with PyTorch. The section ends with a suggestion to explore the various image transformation options available in PyTorch.

21:00:00 - 22:00:00 In this PyTorch course, the instructor explains how to load and transform image data into tensors, create and customize data loaders for training and testing, and create a custom data loading class. They demonstrate the functionality of the prebuilt data sets function, image folder, which can be used to customize transforms for all images. They also walk through the steps required to build a custom data loader, including creating a function to get class names and mappings from directories, sub-classing torch.utils.data.Dataset, and overwriting the get item and len methods. While the customization capabilities of data loaders are useful, there is a risk of writing code with errors.

22:00:00 - 23:00:00 This part of the course covers how to create and utilize custom datasets and custom loaders in PyTorch, as well as how to implement data augmentation techniques. The instructor demonstrates how to build a convolutional neural network using the PyTorch library and provides advice on areas to experiment, including hyperparameters such as kernel size and stride. The course also covers how to test the augmentation pipeline and leverage trivial augment techniques to improve model accuracy. The takeaways from the course include the flexibility of PyTorch and the ability to inherit from the base dataset class to create custom data set loading functions.

23:00:00 - 24:00:00 The instructor covers various aspects of PyTorch for deep learning and machine learning, including troubleshooting shape errors in models, using Torch Info to print summaries of PyTorch models, creating train and test step functions for evaluating performance on datasets, and combining these functions into a train function for easier model training. The instructor also discusses timing the training process of a deep learning model, plotting loss curves to track model progress over time, and evaluating model performance by experimenting with different settings, such as adding layers or adjusting the learning rate. Ultimately, these skills will provide a solid foundation for building and evaluating advanced models using PyTorch.

24:00:00 - 25:00:00 In this section of the PyTorch for Deep Learning & Machine Learning course, the instructor discusses the concepts of overfitting and underfitting in models, along with ways to deal with them, such as data augmentation, early stopping, and simplifying the model. They emphasize the importance of evaluating the model's performance over time using loss curves and provide tools for comparing different models' performance. The section also covers how to prepare custom images for prediction and demonstrates how to load an image into PyTorch using torch vision.io and convert it to a tensor. The instructor notes that before passing the image through a model, it may need to be resized, converted to float32, and put on the right device.

25:00:00 - 26:35:00 This part of the PyTorch course covers various topics such as data types and shapes, transforming image data using PyTorch's transform package and making predictions on custom data using a pre-trained model. To ensure that data is in the correct format before being fed to the model, it is important to preprocess it, scaling it to be between 0 and 1, transform it if necessary and check that it has the correct device, data type, and shape. The instructor also encourages learners to practice by doing the PyTorch custom data set exercises and offers solutions as references. The instructor also mentions that there are five additional chapters to explore in learnpytorch.io, covering topics such as transfer learning, pytorch model experiment tracking, pytorch paper replicating, and pytorch model deployment.

Part 1

• 00:00:00 In this section of the video, the instructor, Daniel Bourke, introduces viewers to the PyTorch course and sets up the expectations for the beginner-friendly video tutorial. The focus of the course will be on implementing machine learning concepts in PyTorch, a Python-based framework, where viewers will learn by writing code. Bourke mentions that the course will cover important topics such as transfer learning, model deployment, and experiment tracking, and if viewers want to learn more about PyTorch, there are additional resources available at learn pytorch.io. Finally, Bourke defines machine learning as the process of turning data into numbers and finding patterns using algorithms and math, and explains that the focus of the course is on writing code, but viewers can find extra resources if they want to dive deeper into the math behind the code.
  
  
• 00:05:00 In this section, the difference between traditional programming and machine learning is explained. Traditional programming involves writing rules for a task, while machine learning algorithms figure out the rules based on input and output data. The reason to use machine learning over traditional programming is for complex problems where it becomes cumbersome to write all the rules manually. Machine learning can be used for anything as long as it can be converted into numbers, but Google's number one rule of machine learning is to first try to build a simple rule-based system before turning to machine learning.
  
  
• 00:10:00 In this section, the instructor discusses what deep learning is good for and what it is not good for. Deep learning is helpful for tackling problems that require long lists of rules, handling continually changing environments, and discovering insights within large datasets. However, deep learning is not ideal for tasks that require explainability since the patterns learned by a deep learning model are typically uninterpretable by humans. Additionally, if a simple rule-based system can solve the problem at hand, it may be better to use that instead of deep learning.
  
  
• 00:15:00 In this section of the full course on PyTorch for deep learning and machine learning, the instructor compares machine learning and deep learning. Traditional machine learning techniques, like gradient boosted machines such as xg boost, are best for structured data in rows and columns. For unstructured data, like natural language or images, deep learning is generally a better choice. Deep learning models are probabilistic, meaning they make a bet on the outcome, while rule-based systems produce predictable outputs. Additionally, deep learning models require a large amount of data to produce great results, but there are techniques to get good results with less data.
  
  
• 00:20:00 In this section, the instructor explains the difference between structured and unstructured data and the algorithms used for each type in machine learning and deep learning. Structured data works well with shallow algorithms such as random forest and gradient boosted machines, whereas unstructured data requires neural networks. The instructor then delves into the types of neural networks such as fully connected neural networks, convolutional neural networks, recurrent neural networks, and transformers. The instructor advises that once the foundational building blocks for neural networks are learned, the other styles of neural networks become easier to understand. The section ends with the instructor encouraging the viewer to research and form their own definition of neural networks before the next video.
  
  
• 00:25:00 In this section of the PyTorch course, the instructor provides an overview of neural networks and their anatomy. Neural networks consist of an input layer, in which input data is turned into numbers, multiple hidden layers, which manipulate and learn patterns in the data, and an output layer, which outputs the learned representations. The number of nodes in the hidden layers can be customized, and the neural network learns the representations, also called features or weights, on its own. Different types of neural networks can be used for different problems, such as CNNs for images and transformers for natural language processing. Once the neural network outputs its representations, they can be converted into human understandable format. The anatomy of a neural network can be also customized, with the number of layers ranging from a few to over a hundred.
  
  
• 00:30:00 In this section, the anatomy of a neural network is explained. Neural networks consist of an input layer, one or more hidden layers, each with many neurons or hidden units, and an output layer. Patterns and datasets are transformed into numerical data through the use of linear and non-linear functions. The patterns are drawn using a combination of linear and nonlinear functions to produce a desired outcome. Different learning paradigms are discussed, including supervised learning, where data and labels are used together, and unsupervised learning, where only data is used. Self-supervised learning algorithms use data to find patterns, while transfer learning involves using patterns that have already been learned in a different model.
  
  
• 00:35:00 In this section, the instructor discusses the different paradigms of machine learning, specifically supervised learning, transfer learning, and reinforcement learning. While the focus will be on supervised learning and transfer learning in this course, the instructor encourages learners to explore reinforcement learning in their own time. Additionally, the instructor provides examples of how deep learning is used, such as recommendation systems, translation, speech recognition, and computer vision. The versatility of machine learning is emphasized, as anything that can be converted into numbers and programmed to find patterns can be used in machine learning algorithms.
  
  
• 00:40:00 In this section of the video, the instructor discusses potential applications of deep learning, such as object detection using computer vision to capture hit and run incidents to identify the culprit's car. The instructor explains the concept of natural language processing and how it is used in spam detection. The video then moves on to cover the foundations of PyTorch, the most popular research deep learning framework that allows for fast deep learning in Python, access to pre-built models, and accelerated GPU performance. The PyTorch website is identified as a necessary resource for the course.
  
  
• 00:45:00 In this section, the instructor discusses PyTorch, which he claims is the most popular deep learning research framework. He cites Papers with Code, which tracks machine learning papers that have code, to demonstrate PyTorch's popularity, showing that 58% of the 65,000 papers that the site has tracked are implemented with PyTorch. Additionally, he provides various reasons for PyTorch's popularity, including its use by companies such as Facebook/Meta, Tesla, Microsoft, and OpenAI, as well as its ability to standardize research methodologies. The instructor also highlights Francois Chale's tweet, which praises PyTorch as a tool that anyone can use to solve problems without requiring significant investment or an engineering team. Overall, the instructor concludes that PyTorch is a research favorite with a diverse ecosystem and a high adoption rate among industry heavyweights.
  
  
• 00:50:00 In this section of the PyTorch for Deep Learning & Machine Learning course, the instructor discusses the various applications of PyTorch, such as in agriculture and social media platforms like Facebook and Microsoft. Moreover, he explains how PyTorch enables users to run machine learning code on GPUs, which are highly efficient at performing numerical calculations, especially in parallel processing. PyTorch leverages CUDA to enable machine learning code to run on NVIDIA GPUs, and while TPUs are available, GPUs tend to be more popular when running PyTorch code. Additionally, he leaves the question of "What is a tensor?" to the audience to research, with the following section to cover the topic in depth.
  
  
• 00:55:00 In this section, the instructor explains that tensors are the fundamental building block of PyTorch, and they could be anything that represents numbers. The input data is numerically encoded to turn it into a tensor, which could be an image or a million images. The input tensor(s) is then passed to a neural network that manipulates it through mathematical operations and outputs another tensor, which is then converted back into a human-understandable format. The reason for using PyTorch and searching for answers to questions online is emphasized, as well as an introduction to specific topics that will be covered in the course.

Part 2

• 01:00:00 In this section, the instructor highlights the topics covered in the PyTorch course. The course starts with the basics of PyTorch, focusing on tensors and tensor operations, then moves to pre-processing data, building and using pre-trained deep learning models, fitting a model to a dataset, making predictions, and evaluating the model's predictions. The instructor explains the workflow, including getting the data ready, choosing a pre-trained model, picking a loss function and optimizer, building a training loop, fitting the model, and improving the model through experimentation. Lastly, the instructor emphasizes the importance of coding along and exploring and experimenting with the code while linking extracurricular resources to learn more about the theory behind the code.
  
  
• 01:05:00 In this section, the instructor of the PyTorch for Deep Learning & Machine Learning course advises learners to approach the course with the mind of a scientist and a chef. He emphasizes the importance of experimentation and visualization to understand the data in deep learning. Additionally, the instructor encourages learners to ask questions and do the exercises provided, as well as to share their work with others to help with their own learning and that of others. Lastly, he advises learners to avoid overthinking and saying they can't learn, urging them to avoid having their brain catch on fire. Finally, he directs learners to the fundamental resources required for the course, including a GitHub repo with all the required materials.
  
  
• 01:10:00 In this section, the speaker explains the resources available for the course and how to utilize them effectively. The course materials, including code and notebooks, are available on GitHub, while the course Q&A can be found on the discussions tab of the same repository. Additionally, there is an online book available at learnpytorch.io. For PyTorch-related questions that are not course-specific, the PyTorch forums and website are highly recommended. The speaker then introduces Google Colab, which will be the main tool used throughout the course, and encourages users to code along by accessing it through colab.research.google.com.
  
  
• 01:15:00 In this section, the instructor explains how to use Google Colab to create a new notebook and write PyTorch code. Google Colab provides benefits such as the ability to use GPU or TPU acceleration for faster compute time, as well as pre-installed PyTorch and other common Python data science packages. The instructor links to a resource notebook on learnpytorch.io and provides a GitHub repository where learners can ask questions related to the course. The instructor also mentions that while they use the paid version of Google Colab, the free version is sufficient for completing the course.
  
  
• 01:20:00 In this section, the video introduces how to set up PyTorch using Google Colab or by referring to the setup document for local setup. The recommended setup for completing the course is PyTorch 1.10 and CUDA 11.3. The video also suggests using a split-window approach to follow along and create a notebook for practice. The main focus of the video is an introduction to tensors, the main building block for deep learning, providing examples of creating a scalar tensor filled with the number seven, and how to access PyTorch's documentation for torch.tensor.
  
  
• 01:25:00 In this section, the instructor explains the basics of PyTorch tensors, starting with the creation of tensors using torch.dot.tensor. He encourages the learners to peruse through the PyTorch documentation to learn more about the library. Moving on, the instructor explains the attributes of scalars, vectors, and matrices. He clarifies that a scalar has no dimensions and is just a single number, while a vector has one dimension, usually represented as a magnitude and direction. A matrix is the next step up and has two dimensions represented by two pairs of square brackets. He explains the difference between dimension and shape, and how to find the shape of a vector in terms of its dimensions.
  
  
• 01:30:00 In this section, the instructor introduces tensors in PyTorch and explains that they are the fundamental building blocks of deep learning neural networks. The instructor demonstrates how to create tensors with different dimensions, ranging from a scalar to a matrix to a tensor with three square bracket pairings. The instructor explains that the number of dimensions is indicated by the number of square bracket pairings, and the shape is determined by the number of elements in each dimension. Additionally, the instructor notes that although writing out tensors by hand is tedious, it is important to understand how they work since PyTorch uses them extensively.
  
  
• 01:35:00 In this section, the instructor discusses the importance of random tensors in PyTorch for machine learning and deep learning. Starting with tensors full of random numbers and then adjusting them to better represent the data is a key concept in neural networks. To create a random tensor in PyTorch, the instructor shows how to use the torch.rand function and explains that "size" and "shape" are two different versions of the same thing. The naming convention for variables in deep learning, such as scalars and vectors being lowercase and matrices and tensors being uppercase, is also briefly discussed.
  
  
• 01:40:00 In this section, the instructor demonstrates how to create random tensors using PyTorch and explains that various types of data, including images, can be represented in tensor format. The instructor explains that PyTorch simplifies the process of creating tensors and, in many cases, handles the process behind the scenes. They demonstrate how to create a random tensor with a shape similar to that of an image tensor and explain that images are commonly represented as tensors with color channels, height, and width. The instructor emphasizes that almost any type of data can be represented as a tensor, making PyTorch a powerful tool for deep learning and machine learning applications.
  
  
• 01:45:00 In this section, the instructor introduces how to create tensors of zeros and ones, and how to create a range of tensors. The tensor of all zeros is useful for creating a tensor mask, which can zero out certain columns of a tensor. The tensor of all ones can also be useful in some situations. The instructor explains how to use torch.range, but warns that it may be deprecated in some versions of PyTorch, and suggests using a range function instead.
  
  
• 01:50:00 In this section, the PyTorch functionality of creating tensors using a range and ones having the same shape as another tensor is explained. The range of tensors is created using torch.arange() where start, stop, and step can be defined. Similarly, torch.zeros_like() is used to create a tensor of zeros with the same shape as the input tensor. The section then introduces tensor data types in PyTorch, specifying that the default type is float 32, even if none is specified.
  
  
• 01:55:00 In this section, we learn about the important parameters when creating tensors in PyTorch, such as data type, device, and requires grad. We discover that data type refers to the level of precision in computing and commonly interact with 32-bit floating point and 16-bit floating point tensors. Single precision is 32-bit and half-precision is 16-bit, with 32-bit being the default tensor type in PyTorch. The note on tensor data types is essential as it is one of the three significant errors we may run into while using PyTorch and deep learning. The other two errors include tensors not having the right shape and not being on the right device.

Part 3

• 02:00:00 In this section, the instructor discusses the importance of maintaining the correct shape and device when working with tensors in PyTorch. If tensors have different shapes or are located on different devices (CPU or GPU), errors can occur. Additionally, the instructor explains the role of the "grad" parameter in tracking gradients during numerical calculations. The lesson includes a challenge for viewers to create tensors of different data types and test the impact of multiplying tensors of different types. The instructor warns that while some operations may not result in errors, others can lead to data type issues, particularly when training large neural networks.
  
  
• 02:05:00 In this section of the video, the instructor goes over how to troubleshoot tensor operations and ensure that tensors have the correct data type and shape for use in machine learning models. They demonstrate how to check the data type, shape, and device of a tensor using PyTorch, using the commands tensor.Dtype, tensor.shape, and tensor.device. The instructor also notes that PyTorch can throw errors if tensors are not in the correct data type or shape, and shows how to change the data type if needed. Lastly, they compare the size and shape commands and note that they are interchangeable, with one being a function and the other being an attribute.
  
  
• 02:10:00 In this section, the instructor goes over manipulating tensors in PyTorch, specifically tensor operations like addition, subtraction, multiplication, division, and matrix multiplication. These operations are important for building neural networks as they help resolve the most common issues with building deep learning models. In addition, neural networks combine these functions in various ways to analyze and adjust the numbers of a random tensor to represent a dataset. The instructor demonstrates how to perform the basic tensor operations of addition, multiplication, and matrix multiplication using PyTorch code examples.
  
  
• 02:15:00 In this section, the instructor covers tensor operations using PyTorch and introduces the concept of matrix multiplication. They demonstrate how to perform element-wise multiplication, addition, and subtraction using Python operators as well as PyTorch built-in functions. The instructor issues a challenge to viewers to search and understand matrix multiplication before diving into it. They explain that there are two main ways of performing multiplication in neural networks, element-wise and matrix multiplication, which is also referred to as the dot product. The instructor provides examples of both types of multiplication using matrices and emphasizes that matrix multiplication is one of the most common tensor operations in neural networks.
  
  
• 02:20:00 In this section, the instructor explains the difference between element wise and dot product multiplication in PyTorch. To demonstrate the concept, the instructor goes through a step-by-step process of multiplying two matrices, highlighting how each element is multiplied and added to get the final result. Next, the instructor shows how to perform element wise multiplication using a rudimentary example, followed by matrix multiplication using the torch dot mat mall function. The section also covers how to perform matrix multiplication using a for loop and explains the difference in performance between the two methods.
  
  
• 02:25:00 In this section, the video explains the benefits of vectorization over for loops in PyTorch, using the example of matrix multiplication. The torch method torch dot matmore is shown to be 10 times faster than using a for loop for small tensors. However, the video cautions that two main rules must be satisfied for larger tensors in order to avoid shape errors in matrix multiplication. The first rule is that the inner dimensions of the two tensors must match.
  
  
• 02:30:00 In this section, the instructor explains the rules of matrix multiplication and how to avoid common shape errors when multiplying tensors. The first rule is that the inner dimensions of the matrix must match. To demonstrate this, the instructor creates a tensor of size 3x2 and attempts to multiply it by another tensor that doesn't have the same inner dimensions resulting in an error. The second rule is that the resulting matrix has the shape of the outer dimensions. The instructor gives examples of matrix multiplication with different tensor shapes and dimensions, and how they result in different matrix shapes. The instructor encourages viewers to use a website for practicing matrix multiplication as a challenge before the next video.
  
  
• 02:35:00 In this section, the instructor discusses shape errors in neural networks, which are one of the most common errors in deep learning. Since neural networks are composed of several matrix multiplication operations, even a slight tensor shape error can lead to a shape error. The instructor then creates two tensors, tensor a and tensor b, and tries to perform a matrix multiplication between them, leading to a shape error. To fix this error, the instructor introduces the concept of a transpose, which switches the axes or dimensions of a given tensor, and demonstrates how it can be used to adjust the shape of tensors in PyTorch code.
  
  
• 02:40:00 In this section, the instructor explains the concept of transposing tensors and its importance in matrix multiplication. Transposition rearranges the elements of a tensor without changing its underlying data, and it is denoted by "dot t." The instructor also demonstrates how a matrix multiplication operation works when tensor b is transposed and highlights the importance of this operation in neural networks and deep learning. The process of transposing tensors is illustrated visually, and the instructor provides step-by-step code examples to help students understand and practice the concept.
  
  
• 02:45:00 In this section, the instructor explains matrix multiplication using PyTorch and a website called Matrix Multiplication. He created two tensors, tensor a and tensor b, and showed that their multiplication results in a new tensor with a specific output shape. He challenges viewers to transpose tensor a instead of tensor b and see the results. Moving on, the instructor covers tensor aggregation, showing how to find the min, max, mean, and sum of a tensor using PyTorch methods. He also explains how tensor aggregation helps in reducing the number of elements in a tensor.
  
  
• 02:50:00 In this section of the PyTorch tutorial, the instructor showcases how to solve one of the most common errors in PyTorch, which is having the wrong data type. He demonstrates this by creating a tensor of the data type long, which prevents the use of the torch mean function. He then explains how to convert the tensor to float 32, which is required by the mean function, by using the x.type() method. In addition to finding the min, max, mean, and sum of the tensor, the instructor also sets a challenge for finding the positional min and max, which will be covered in the next video.
  
  
• 02:55:00 In this section, the use of argmin and argmax functions in PyTorch for finding the positional min and max of a tensor was explained. The argmin function returns the position in the tensor that has the minimum value, while the argmax function returns the position in the tensor that has the maximum value. These functions are helpful when defining the minimum or maximum values of a tensor is not necessary, but only the position of those values. Additionally, the concepts of reshaping, stacking, squeezing, and unsqueezing of tensors were introduced, which are useful for managing shape mismatches in machine learning and deep learning.

Part 4

• 03:00:00 In this section, the instructor explains the different tensor manipulation methods in PyTorch, such as reshape, view, stacking, squeeze, unsqueeze, and permute. Reshape changes the shape of an input tensor, view returns a view of a tensor with a different shape, stacking combines multiple tensors together either vertically or horizontally, squeeze removes all dimensions that are equal to 1, and unsqueeze adds a new dimension with a size of 1. Finally, permute swaps the dimensions of a tensor. The instructor provides code examples to demonstrate each of these methods and emphasizes how important tensor shape manipulation is in machine learning and deep learning.
  
  
• 03:05:00 In this section, the video tutorial explores how to reshape and view PyTorch tensors. Reshaping requires compatibility with the original size and can be done using either the 'reshape' or 'view' functions. It's important to note that 'view' shares the same memory with the original tensor. In addition, the 'stack' function concatenates tensors along a new dimension, and the default dimension is zero. Users are advised to save their work frequently as errors may occur while using Google CoLab or any other forms of Jupyter Notebooks.
  
  
• 03:10:00 In this section, the instructor introduces the concepts of squeeze and unsqueeze in PyTorch. To practice using these methods, the viewer is encouraged to look up the documentation and try them out. The instructor demonstrates the squeeze method where single dimensions are removed from a target tensor. To visualize the changes made to tensors during these operations, the instructor suggests printing out each change and checking the size of the tensor. Additionally, the instructor emphasizes the importance of practicing these concepts multiple times to gain familiarity with them.
  
  
• 03:15:00 In this section, the instructor explains the concept of adding and removing dimensions in PyTorch tensors using the methods "squeeze" and "unsqueeze". He demonstrates the effects of these methods by adding and removing dimensions from tensors and printing their shapes. The instructor also introduces the "permute" method, which rearranges the dimensions of a target tensor in a specified order. He provides an example of how permute can be used with images and discusses the importance of turning data into numerical representations in deep learning.
  
  
• 03:20:00 In this section, the instructor teaches about permuting a tensor by rearranging its dimensions using the permute() method in PyTorch. The example given is an image tensor where the color channel dimension is moved to the first index. The instructor explains that a permuted tensor is just a view and shares the same memory as the original tensor, demonstrated by updating a value in the original tensor and seeing the same value copied across to the permuted tensor. The section also covers indexing in PyTorch and how it is similar to indexing with NumPy, another popular numerical computing library.
  
  
• 03:25:00 In this section, the instructor introduces how to import torch and shows how to create a small range and reshape it in a compatible way. The tutorial then delves into indexing with tensors and shows how to index on the first and second dimensions. The tutorial also reveals the functionality of using a semicolon to select all of a target dimension. The section ends with a challenge to rearrange the code to get number nine.
  
  
• 03:30:00 In this section, the instructor demonstrates how to select specific values from a tensor using PyTorch. The examples involve selecting elements from different dimensions of the tensor by specifying the appropriate index values. The instructor then challenges viewers to try indexing the tensor to return specific values. In the next section, the instructor explains how PyTorch tensors interact with the popular scientific numerical computing library, NumPy. Since PyTorch requires it, there is functionality built in to allow for easy transition between NumPy arrays and PyTorch tensors.
  
  
• 03:35:00 In this section, the video discusses how to convert data from NumPy to PyTorch tensors and vice versa. To go from NumPy to PyTorch, the torch.fromNumPy method is used on the NumPy array, but it should be noted that PyTorch's default data type is float32 while NumPy's default is float64. Therefore, there may be a need to specify the data type when converting. It is also important to note that when changing the value of the original NumPy array, it does not change the value of the PyTorch tensor if it was created using the fromNumPy method. To go from PyTorch to NumPy, the method torch.tensor.numpy() can be used.
  
  
• 03:40:00 In this section, the video discusses how to go between PyTorch and NumPy and the default data types of each. The default data type of PyTorch is float32, while the default data type of NumPy is float64, and if you change the data type in PyTorch, the NumPy tensor will reflect the original data type. The video also covers the concept of reproducibility in neural networks and the use of random seeds to reduce randomness in experiments. By setting a random seed, the randomness is flavored, and the computer becomes more deterministic, allowing for more reproducible results.
  
  
• 03:45:00 In this section, the instructor introduces the concept of randomness and reproducibility in PyTorch. Two random tensors are created using the torch.rand function, and their values are printed and compared. The instructor explains the concept of random seed, which is used to create reproducible randomness in PyTorch. The random seed value can be set to a numerical value of choice, such as 42, and then used in various random functions to get flavored randomness. It is important to note that if the torch.manual_seed function is used, it generally only works for one block of code in a notebook.
  
  
• 03:50:00 In this section of the video, the importance of reproducibility in machine learning and deep learning is emphasized, and the concept of a random seed is explained. The manual seed is a way to flavor the randomness of PyTorch random tensors and make them reproducible. The PyTorch reproducibility document is recommended as a great resource for learning about reproducibility. The section also discusses running PyTorch objects on GPUs for faster computations and how to get access to GPUs, including using Google Colab for a free GPU, Google Colab Pro for faster GPUs and longer run time, and Google Colab Pro Plus for more advanced advantages.
  
  
• 03:55:00 In this section, the instructor explains different ways to access GPUs for deep learning and machine learning tasks. The options are to use Google Colab, upgrade to Colab Pro, use your own GPU or use cloud computing services like GCP, AWS, or Azure. The instructor recommends starting with Google Colab, which is easy and free to use. However, if you need more resources or want to run bigger experiments, you may want to upgrade or use your own GPU or cloud computing. The instructor also shows how to get a GPU in Google Colab by changing the runtime type and checking for GPU access with PyTorch.

[MetaQuotes]

PyTorch for Deep Learning & Machine Learning – Full Course (description for parts 5-10)

PyTorch for Deep Learning & Machine Learning – Full Course

Part 5

• 04:00:00 In this section, the instructor explains how to check for GPU access with PyTorch and set up device agnostic code. Using the command "torch.cuda.is_available()", the user can check if PyTorch can access the GPU. Additionally, in order to run PyTorch, the device variable should be set to use the GPU, if available, or default to the CPU. It's also important to set up device agnostic code, which allows PyTorch to run on either the CPU or GPU, depending on what's available, by setting up "args.device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')" in Python scripts. The instructor emphasizes that setting up device agnostic code is a best practice when working with PyTorch since it allows the code to be run on different devices.
  
  
• 04:05:00 In this section, the instructor discusses how using GPUs can result in faster computations and be beneficial for machine learning models that work with numerical calculations like tensor operations. To use the GPU, tensors and models need to be transferred to it, and PyTorch makes it easy to do so with the `to` method. The code can be made device agnostic so that it will run regardless of whether a GPU is available or not. Similarly, tensors can also be moved back to the CPU if needed, and the `cpu()` method can be used for this. The instructor emphasizes that device issues are the third most common error in PyTorch, and that it's a good practice to keep in mind the device type that tensors and models are stored on.
  
  
• 04:10:00 In this section, the instructor discusses the fundamentals of working with PyTorch on the GPU. He explains how to switch between CPU and GPU and how to avoid errors when using NumPy calculations with tensors on the GPU. He also encourages learners to practice what they've learned through a set of exercises and extra curriculum available on learn.pytorch.io. The exercises are based on what has been covered in the previous sections, and learners are encouraged to use the PyTorch documentation to complete them. Finally, the instructor provides tips on how to approach these exercises in Colab by setting up two screens and importing torch.
  
  
• 04:15:00 In this section, the instructor discusses the exercises and extra curriculum in the PyTorch course. The exercises are code-based and include templates for each of the exercises. The extra curriculum is reading-based, and the instructor recommends spending an hour going through the PyTorch basics tutorial, quick start, and tensor sections, as well as watching the "What's a Tensor?" video. The instructor also outlines the PyTorch workflow, which includes preparing the data and turning it into tensors, picking or building a pre-trained model, selecting a loss function and optimizing it, building a training loop, fitting the model, evaluating the model, experimenting and improving, and saving and reloading the trained model. The instructor encourages learners to follow along with the code and documentation, search for resources, try again, and ask questions in the PyTorch forums.
  
  
• 04:20:00 In this section of the video, the instructor begins by opening a new notebook in Colab and titles it "01 PyTorch Workflow." He explains that they will be focusing on coding together and creating a PyTorch end-to-end workflow, which involves data preparing and loading, building a machine learning model in PyTorch, training the model, evaluating the model, and saving/loading the model. The instructor also mentions that they will be using the nn module in PyTorch, which contains all of PyTorch's building blocks for neural networks.
  
  
• 04:25:00 In this section, the instructor discusses using torch.nn in PyTorch by exploring the basic building blocks for computational graphs, which are used in neural networks. Combining these building blocks allows data scientists and machine learning engineers to build various types of neural networks. The instructor stresses the importance of the first step in the PyTorch workflow, which is preparing and loading data into a numerical representation for the model to learn patterns. The type of numerical representation used for encoding data is dependent on the data type. The second step involves building a neural network to learn the patterns in the numerical representation, and then using the learned patterns for specific tasks such as image recognition or spam classification.
  
  
• 04:30:00 In this section, the instructor introduces the game of two parts in machine learning, which involves converting data into numerical representation and building a model to find patterns in that representation. The instructor then creates known data using the linear regression formula to showcase this process. The weight and bias of the formula are used as parameters that a model will learn by looking at different examples. The code in Python is used to create a range of numbers, assign variable X a value, and create a Y formula that equals weight times X plus bias. The length and value of X and Y are viewed, and the first ten values of X and Y are displayed.
  
  
• 04:35:00 In this section, the instructor discusses the importance of splitting data into training and test sets in machine learning. He uses the analogy of university courses and exams to explain the concept. The training set is akin to all the course materials, the validation set is like a practice exam, and the test set is the final exam. The goal is to achieve generalization, so that the model can adapt to unseen data. The instructor emphasizes that splitting data correctly is vital in order to create an accurate model.
  
  
• 04:40:00 In this section of the PyTorch full course, the instructor discusses the importance of generalization in machine learning models and the three data sets commonly used in training and testing: training, validation and testing sets. He also explains the common percentage splits used for each of these sets, with the training set usually having 60-80% of the data and the testing set having 10-20%. The instructor then demonstrates how to create a training and test set using a sample data set with X and Y values using indexing to select the appropriate number of samples for each split. Finally, he explains that while there is often a use case for a validation set in more complex data sets, the training and testing sets are the most commonly used.
  
  
• 04:45:00 In this section, the instructor emphasizes the importance of visualizing data by writing a function called "plot predictions" which will be used to compare the training and test data. The function takes in X train, Y train, X test, Y test, and predictions as parameters and then plots the training data in blue using a scatter plot with the matplotlib library. The testing data is then plotted in green using the same scatter function. The function also checks if there are any predictions and if so, plots them using the scatter function as well. By visualizing the data, it becomes easier to understand and interpret.
  
  
• 04:50:00 In this section of the video, the instructor discusses the process of training and evaluating machine learning models. They explain that the objective is to train the model on the training data in order to accurately predict the values of the test data. They demonstrate this using a simple linear data set, with the training data plotted in blue and the testing data in green. The instructor then introduces the concept of linear regression and sets the stage for the next section, where they will build a PyTorch model for linear regression. They also provide some troubleshooting tips for Google Colab.
  
  
• 04:55:00 In this section of the video, we learn how to create a linear regression model using pure PyTorch. The instructor explains that PyTorch is built on top of the nn.module, which is like the Lego building bricks of PyTorch models. Almost everything in PyTorch inherits from nn.module, and modules can contain other modules, which makes it easy to build complex neural networks. The instructor then walks us through the process of creating a constructor with the init function, creating a weights parameter using nn.parameter, and setting it to random parameters using torch.rand. The instructor also explains how to set requires_grad and dtype.

Part 6

• 05:00:00 In this section, the instructor explains how to create a linear regression model using PyTorch. They begin by creating a class for the model and initializing it with parameters for the weights and bias, which are automatically added to the list of parameters when assigned to a module attribute. They then create a forward method to define the computation in the model, which is based on the linear regression formula. The goal of the model is to update the random parameters to represent the pattern of the training data through gradient descent, which is the premise of machine learning.
  
  
• 05:05:00 In this section, the instructor discusses the process of adjusting random values to better represent the desired values of weight and bias in the data, which is accomplished using two main algorithms: gradient descent and backpropagation. The use of 'requires grad equals true' is explained as keeping track of gradients via computations done using the model, nudging the algorithm in the right direction. The importance of object-oriented programming and the role of PyTorch in implementing these algorithms are emphasized, with additional resources suggested to help gain intuition for the code. The instructor also highlights the fact that while the current model deals with simple datasets with known parameters, more complex datasets often have parameters defined by modules from nn for us.
  
  
• 05:10:00 In this section, the instructor explains the main takeaways from creating the first PyTorch model. Every model in PyTorch inherits from nn.modgable and should override the forward method to define the computation that is happening inside the model. Further, when the model learns, it updates its weights and bias values via gradient descent and back propagation using the torch.auto grad module. The instructor recommends checking out two videos linked in the transcript for a complete understanding of this concept. Additionally, the instructor introduces some PyTorch model building essentials such as the torch.nn module that contains all building blocks necessary for neural networks.
  
  
• 05:15:00 In this section, the instructor explains the fundamental modules in PyTorch, including torch.nn, torch.nn.module, torch.optim, and torch.utils.dataset. The torch.nn.module is the base class for all neural network modules and requires the forward method to be overwritten, which defines what happens in the forward computation. Torch.optim contains algorithms to optimize the values of the model, which begins with random values and adjusts to better represent the ideal values. The instructor also mentions the PyTorch cheat sheet as a helpful resource for further exploration of the library.
  
  
• 05:20:00 In this section, the instructor adds some color and code to the PyTorch workflow and covers important PyTorch modules used for creating datasets, building and training models, optimizing model parameters, evaluating the model, and improving through experimentation. The instructor then shows how to check the contents of a PyTorch model by creating an instance of the linear regression model and using dot parameters to see the values tensor. The instructor also sets a random seed to create the parameters with consistent values.
  
  
• 05:25:00 section, we learned about how deep learning models are initialized with random values for weights and bias parameters. We also learned about the importance of using random seed values for reproducibility. The fundamental premise of deep learning is to adjust these random values to be as close as possible to ideal values through gradient descent and back propagation, using training data. In the next section, the video will cover making predictions with the model's random parameter values.
  
  
• 05:30:00 this section, the video explains the process of testing a PyTorch model's predictive power. The model's forward method takes input data X and passes it through the model to make predictions. The video demonstrates how to test the model's predictive power by inputting X test, which consists of 10 variables, and observing the model's output Y pred. The video also addresses a common error that can occur during the creation of a PyTorch model and provides a fix for it.
  
  
• 05:35:00 In this section, we see the model's predictions by running the test data using the Ford method that was defined earlier. The predictions seem to be shockingly far from the ideal predictions. The code also introduces torch inference mode, which is a context manager used to disable gradient tracking when making predictions, allowing PyTorch to keep track of less data and make predictions faster. While torch no grad can do something similar, inference mode has some advantages over no grad, as explained in the PyTorch documentation and a Twitter thread provided in the video. Therefore, inference mode is currently the favored way of doing inference.
  
  
• 05:40:00 this section, the video explains the importance of using torch.inference_mode context manager when making predictions in PyTorch, as it ensures that the model is in inference mode instead of training mode. The video also highlights that initializing a model with random parameters could result in poor performance, and provides some options for initialization, such as using zero values or transferring parameters from another model. The main focus of the video, however, is on training a model by moving from unknown parameters to known parameters using a loss function, which measures how poorly the model's predictions are performing. The video notes that the terms "loss function", "cost function", and "criterion" are often used interchangeably in machine learning.
  
  
• 05:45:00 In this section, the instructor introduces the concept of a loss function, which is used to measure how wrong a model's predictions are compared to the ideal outputs. The instructor uses the example of measuring the distance between red and green dots to explain how a loss function can be calculated. The video also covers the importance of an optimizer, which takes into account the loss of a model and adjusts its parameters, such as weight and bias values, to improve the loss function. The section concludes by explaining that the principles of a loss function and optimizer remain the same whether dealing with models with two parameters or models with millions of parameters, and whether computer vision models or simple models like those that predict dots on a straight line.
  
  
• 05:50:00 In this section of the PyTorch for Deep Learning & Machine Learning course, the instructor explains the importance of using an optimizer to nudge the parameters of a model towards values that lower the loss function in order to improve the accuracy of the predictions. PyTorch has built-in functionality for implementing loss functions and optimizers, and the instructor focuses on the L1 loss, also known as mean absolute error, which measures the absolute difference between predicted and actual values. The instructor provides a colorful graph to illustrate the mean absolute error and shows how to implement the loss function using PyTorch's NN module. The objective for training a model will be to minimize the distances between predicted and actual values and in turn, minimize the overall value of mean absolute error.
  
  
• 05:55:00 In this section, the instructor discusses the role of the optimizer in machine learning, which works in tandem with the loss function to adjust model parameters, like weight and bias, to minimize the loss. PyTorch has torch.optim, where various optimization algorithms are available, such as Stochastic Gradient Descent (SGD) and Adam. They both randomly adjust model parameters to minimize loss, but it's a matter of picking the most appropriate one for a specific problem. Most opt for SGD, which starts with random adjustments and then continues adjusting in the direction that minimizes loss until no further adjustments can be made. The optimizer requires two arguments, params, or what parameters the optimizer should optimize, and the learning rate (LR), the most important hyperparameter to set when optimizing.

Part 7

• 06:00:00 In this section, the instructor explains what model parameters and hyper parameters are and their role in the deep learning process. Model parameters are values that the model sets while hyper parameters are values set by the data scientist or machine learning engineer. The learning rate is a hyper parameter, and its value determines the size of the change in the parameters during optimization. A small learning rate results in small changes while a large learning rate results in large changes. The instructor also talks about the importance of choosing the appropriate loss function and optimizer for the specific problem. Finally, the instructor moves on to explain the process of building a training loop in PyTorch.
  
  
• 06:05:00 In this section, the instructor discusses the steps needed to build a training loop and a testing loop in PyTorch. The first step involves looping through the data multiple times to improve predictions and minimize loss by making forward passes through the model. The instructor explains that the forward pass is when the data moves through the model's forward functions, and the loss is calculated by comparing the model's predictions to ground truth labels. The instructor then introduces the optimizer and explains that the backward pass calculates the gradients of each parameter with respect to the loss, allowing the optimizer to adjust the model's parameters to improve the loss through gradient descent. The instructor highlights that PyTorch implements back propagation and the math of gradient descent, making it easier for those with limited math backgrounds to learn about machine learning.
  
  
• 06:10:00 In this section, the instructor introduces the concept of gradient descent, which is used to optimize the model parameters in machine learning. Using the example of a hill, the instructor explains how the model needs to move towards the direction where the slope is less steep to reach the bottom of the hill, which represents a zero loss. The instructor then moves on to write some code for running gradient descent, which involves setting the number of epochs and setting the model to training mode using the "requires grad equals true" parameter. The instructor also mentions that different modes are available for pytorch models, and encourages viewers to experiment with different settings.
  
  
• 06:15:00 In this section of the video, the instructor discusses the implementation of the forward pass for training a PyTorch model. The forward pass involves passing data through the model's forward function to make predictions, which are then compared to the actual training values using the MAE loss function. The optimizer.zero_grad() function is also introduced, which sets all gradients to zero before computing the loss backward and updating the model parameters using gradient descent. These steps are crucial in understanding how a model learns and will be further optimized and functionized in later sections of the course.
  
  
• 06:20:00 In this section, the instructor goes over the five major steps of a training loop in PyTorch, which includes the forward pass, calculating the loss, zeroing the optimizer gradients, performing back propagation, and stepping the optimizer through gradient descent. The instructor notes that the order of these steps can sometimes be ambiguous, but it's important to keep the optimizer step after back propagation. The instructor also explains why the optimizer gradients need to be zeroed in each iteration to prevent accumulation across loops. The instructor suggests practicing writing a training loop to better understand these steps and provides a song and extra resources for further learning.
  
  
• 06:25:00 In this section of the video, the presenter recaps the steps in a training loop in PyTorch, which involves forward pass, calculating the loss value, zeroing the optimizer gradients, and performing back propagation on the loss function. The training loop helps the model learn patterns on the training data, while the testing loop evaluates the patterns on unseen data. The presenter also explains why we zero the optimizer gradients and introduces the concept of back propagation, which computes the gradient of the loss function.
  
  
• 06:30:00 In this section, the instructor explains the concept of gradients and loss function curves in PyTorch deep learning. By setting 'requires grad' as true for the parameters, PyTorch is able to track the gradients of each parameter and create a loss function curve for all of them. The goal of back propagation and subsequent gradient descent is to calculate the lowest point of the curve, which represents the minimum loss. The instructor explains the concept of gradients in machine learning and how gradient descent works with step points. By optimizing the zero grad loss backward, optimizing step, and requires grad, PyTorch does much of this work behind the scenes, automatically tracking gradients and finding the bottom of the curve.
  
  
• 06:35:00 In this section, the instructor discusses the optimizer and the learning rate. The optimizer takes the model parameters and creates curves for each parameter using a mechanism called torch autograd for auto gradient calculation to get closer to the bottom of the curve. The learning rate decides how large or small the optimizer changes the parameters with each step, with smaller steps taken as we get closer to convergence. Additionally, the instructor touches upon the five steps involved in training a model, which includes initializing the model, defining the optimizer, the learning rate, forward pass calculation, backward propagation, and the optimizer step. Finally, the instructor mentions that this loop can be transformed into a function, which helps avoid repetitions of codes.
  
  
• 06:40:00 In this section of the "PyTorch for Deep Learning & Machine Learning – Full Course", the instructor emphasizes the importance of writing the training loop in PyTorch, as it is how the model learns patterns and data. The video also provides additional resources on backpropagation and gradient descent for those interested in the mathematical background. The instructor explains that the choice of loss function and optimizer will be specific to each problem and recommends MAE loss and L1 loss for regression problems and binary cross-entropy loss for classification problems. The section ends with a demonstration of the training loop using a model with only two parameters and a single epoch.
  
  
• 06:45:00 In this section, the instructor continues training the machine learning model using PyTorch and shows how the loss function is going down as the model parameters are updated via gradient descent. The instructor emphasizes that a lower loss value indicates better model progress and that the small differences in values due to randomness in machine learning should not be concerning. The instructor then challenges the viewer to run the code for 100 epochs and make predictions to see how low they can get the loss value. Finally, the instructor discusses the importance of testing and teases the next video on writing testing code.
  
  
• 06:50:00 In this section, the instructor discusses the importance of researching and learning new topics using external resources like Google and documentation. They encourage learners to try running the training code for 100 epochs and examine the weight and bias values and predictions. The instructor then goes on to explain the testing code and the purpose of the model.eval() function, which turns off settings in the model not needed for testing, such as dropout and batch norm layers. They also discuss the purpose of torch.no_grad() and how it turns off gradient tracking during testing since no learning is happening at that stage. Finally, the section concludes with writing the forward pass for the model in testing mode.
  
  
• 06:55:00 In this section, the video teaches how to create test predictions and calculate the test loss using model zero in PyTorch. The test predictions are made on the test data set, which the model has never seen before, just like evaluating one's knowledge on materials they have never seen before. The video explains the importance of not letting the model see the test data set before evaluating it to avoid getting poor results. The code prints out the loss and what's happening every 10th epoch while the model is training for 100 epochs, and the loss is seen to decrease with each epoch. The video also discusses the concept of model accuracy, which may be printed out later.

Part 8

• 07:00:00 In this section, the instructor reviews the previous video, in which they trained a model and made predictions on a simple dataset. They then challenge the viewer to find ways to improve the model's ability to align the predicted red dots with the actual green dots, possibly by training the model for longer. The instructor then reruns the code for 100 more epochs and shows significant improvement in the model's test loss and predictions. The instructor emphasizes that this process of training and evaluating models is fundamental to deep learning with PyTorch and will be used in the rest of the course. They also discuss the importance of tracking model progress using an empty list to store useful values.
  
  
• 07:05:00 In this section, the instructor explains why it is important to keep track of loss values and how we can use them to monitor our model's progress and improve upon it in future experiments. The code snippet presented appends the epoch count, current loss value, and current test loss value to different lists so that they can be plotted later. The instructor demonstrates a plot of the loss curves generated from the lists and explains their significance. An ideal loss curve should start high and decrease over time, representing a decreasing loss value.
  
  
• 07:10:00 In this section, the instructor explains how to convert loss values from PyTorch to NumPy to plot them in Matplotlib. He shows that converting them to NumPy is necessary since Matplotlib only works with NumPy. He also explains how to keep track of the training loss and test loss curves and mentions that if they match up closely at some point, it means that the model is converging and the loss is getting as close to zero as possible. The instructor then walks through the testing loop and explains that it is necessary to pass the test data through the model, calculate the test loss value, and print out what's happening during training to keep track of the values of what's going on. Finally, he suggests putting all of these steps into a function and provides an unofficial PyTorch optimization loop song to remember the steps.
  
  
• 07:15:00 be learning about in this section - the three main methods for saving and loading models in PyTorch. The first method, torch.save, allows you to save a PyTorch object in Python's pickle format. The second method, torch.load, allows you to load a saved PyTorch object. And the third method, torch.nn.module.loadStateDict, allows you to load a model's saved dictionary or save state dictionary, which we will explore in the following video. These methods are crucial for saving and reusing models, especially when working with larger models or when needing to share models with others.
  
  
• 07:20:00 In this section, the instructor explains the concept of state dictionaries and their importance in PyTorch. PyTorch stores the important parameters of a model in a dictionary, called state dictionary, that holds the state of the model, including the learnable parameters such as weights and biases. The instructor demonstrates how saving and loading the PyTorch model can be done by saving its state dictionary using torch.save and torch.load methods. Moreover, the instructor provides an additional challenge to the user to read and understand the pros and cons of saving the entire model rather than just the state dictionary. Finally, the instructor shares the PyTorch code for saving the model and creating a folder called models.
  
  
• 07:25:00 In this section of the video, the instructor demonstrates how to save a PyTorch model using the recommended method of saving the state dict. The model is given a name, and the path is created using pathlib library. Once the path is ready, the model state dict is saved using torch.save() function, where the first parameter is the object and the second is the path where the model is to be saved. The instructor shows how the LS command is used to check whether the model is saved in the models directory. The video also provides a guide to downloading the saved model to a local machine or Google Drive. Additionally, the instructor encourages the viewer to challenge themselves by reading ahead on documentation and using the torch.load() function to learn how to load a saved model.
  
  
• 07:30:00 In this section, the instructor talks about loading a PyTorch model and how to use the torch dot load method. The class's previously saved dictionary of parameters from a model will be loaded as a state deck, and this section shows how to create a new instance of the linear regression model class and load the saved state deck into that. The torch nn module's load state deck method allows one to load the state dictionary directly into the model instance, while the torch dot load method takes in F and passes it the model's save path where the previous state deck is saved.
  
  
• 07:35:00 In this section, the instructor goes over saving and loading a model in PyTorch. They test the loaded model by making new predictions with the test data and comparing them to the original model's predictions using the equivalent equals equals function. The instructor troubleshoots the models not being equivalent by making a new set of model predictions and testing equivalence again. They cover the main aspects of saving and loading a model but suggest checking out tutorials for further details. The instructor plans to put together all the steps covered so far in the next few videos.
  
  
• 07:40:00 In this section of the video, the instructor goes through the entire workflow of deep learning using PyTorch, including importing PyTorch, saving and reloading models, and creating device-agnostic code, which allows the code to use the GPU if available, or the CPU by default if not. The instructor encourages viewers to pause and try to recreate the code on their own, while also offering guidance and helpful tips. The video also covers how to create dummy data sets and plot data points, which will be used to build a model that will learn to predict the green dots from the blue dots.
  
  
• 07:45:00 In this section, the instructor demonstrates how to create data using the linear regression formula of y equals weight times features plus bias. They explain that the principles of building a model to estimate these values remain the same, and they proceed to create the x and y features, which will be used to predict training and test values. They also split the data into training and test sets and plot the data to visualize the patterns in the data.
  
  
• 07:50:00 In this section, the instructor introduces the concept of building a PyTorch linear model for the given linear dummy data. They subclass nn.module to create a linear regression model and initialize the parameters using layers. The nn.Linear layer takes in features as input and output, and applies a linear transformation to the incoming data using the same formula as the linear regression model. The input and output shapes of the model are dependent on the data, and the instructor highlights that different examples of input and output features will be seen throughout the course.
  
  
• 07:55:00 In this section, the instructor explains how to use the linear layer in PyTorch as a pre-existing layer to create a model. The linear layer is a form of linear regression y equals x, a transpose plus b, in features, out features. By subclassing nn.module, we can create a linear layer and override the forward method to pass the data through the linear layer, which performs the predefined forward computation. The power of PyTorch's torch.nn is that it creates the parameters for us behind the scenes, and we don't have to initialize them manually. Additionally, the instructor discusses the different names for the linear layer, such as linear transform, probing layer, fully connected layer, dense layer, and intensive flow.

Part 9

• 08:00:00 In this section, the instructor discusses the different layers available in torch.nn including convolutional, pooling, padding, normalization, recurrent, transformer, linear, and dropout. Pre-built implementations of these layers are provided by PyTorch for common deep learning tasks. The section then moves on to training the previously built PyTorch linear model using loss and optimizer functions. The optimizer will optimize the model's weight and bias parameters to minimize the loss function, which measures how wrong the model is. The instructor sets up the L1 loss function and SGD optimizer for this task.
  
  
• 08:05:00 In this section of the video, the instructor discusses the importance of choosing an appropriate learning rate for the optimizer, as too small or too large a step can negatively impact the model's performance. The steps involved in writing a training loop are also explained, which includes doing a forward pass, calculating the loss value, zeroing the optimizer, performing back propagation, and adjusting the weights and biases. Additionally, the instructor suggests using torch.manual_seed() to ensure reproducible results and provides code to print out the training loss and test loss at every 10 epochs.
  
  
• 08:10:00 In this section of the PyTorch course, the instructor explains how to write device agnostic code for data, emphasizing that having all computation on the same device is crucial to avoid errors. The model and the data should be on the same device, which can be CPU or CUDA. By putting the training and test data on the target device using X train and Y train, this creates device agnostic code, which provides more accurate results when training the model. The instructor also explains how to evaluate the model using state decked, demonstrating that the estimated parameters are close to the ideal value. The section ends with a challenge for the users to make and evaluate predictions and plot them on the original data.
  
  
• 08:15:00 In this section, the instructor discusses the importance of turning the PyTorch model into evaluation mode and making predictions on the test data that the model has never seen before. They bring in the plot predictions function to visualize the model's predictions, but they encounter a type error when trying to convert the CUDA device type tensor to NumPy because Matplotlib works with NumPy, not PyTorch. They solve this error by using tensor dot CPU to copy the tensor to host memory first. The instructor also encourages viewers to save and load their trained model using the path module, which they demonstrate by creating a models directory and setting the model path to it.
  
  
• 08:20:00 In this section, the instructor explains how to save and load PyTorch models using the path lib module from Python. First, a model save path is created with the extension .PTH for PyTorch. The model state dictionary is then saved using the torch save method. The instructor notes that viewing the state deck explicitly may not be viable for models with many parameters. To load the saved model, the saved state dictionary is loaded into a new instance of the linear regression model V2 using the load state decked method and passing the file path of the saved PyTorch object. The use of PyTorch's pre-built linear layer and calling it in the forward method is also discussed.
  
  
• 08:25:00 In this section, the instructor finishes up by checking that the loaded model has the same parameters as the saved model by evaluating it using the torch inference mode. They then congratulate the user for completing the PyTorch workflow from building a model, training it, and saving it to reusing it. The instructor then points out that users can find the exercises and extra curriculum in the book version of the course materials at learnpytorch.io. They also provide exercise notebook templates that are numbered by the section and can be found on the PyTorch deep learning GitHub repository under extras and exercises.
  
  
• 08:30:00 In this section, the instructor provides information on how to complete the workflow exercises and find extra resources for the PyTorch course. He emphasizes the importance of trying the exercises out for yourself before looking at any example solutions. The section concludes with a summary of the PyTorch workflow covered, which includes getting data ready, turning it into tensors, building or selecting a model, choosing a loss function and an optimizer, training the model, making predictions, and evaluating the model. The next section focuses on neural network classification with PyTorch, which is one of the biggest problems in machine learning. The instructor provides resources for getting help throughout the course, including the course GitHub discussions page and the PyTorch documentation. He also explains what classification problems are and gives examples such as predicting whether an email is spam or not.
  
  
• 08:35:00 In this section of the PyTorch course, the instructor discusses different types of classification problems in deep learning. Binary classification is when there are only two options, like spam or not spam. Multi-class classification is when there are more than two options, like classifying an image as sushi, steak, or pizza. Multi-label classification is when an example can have more than one label, like assigning tags to a Wikipedia article. The instructor provides real-world examples and explains the concepts thoroughly. He also distinguishes between binary and multi-class classification with examples of classifying images of dogs and cats in a binary classification problem, and classifying images of different animals in a multi-class classification problem.
  
  
• 08:40:00 In this section, the instructor explains the architecture of a neural network classification model and the input and output shapes of a classification model. He emphasizes the importance of numerical inputs for machine learning models and explains how numerical inputs often come in different shapes depending on the data. He also discusses the process of creating custom data for fitting and predicting and covers steps involved in modeling for neural network classification. Additionally, the instructor explains how to set up a loss function and optimizer for a classification model, create training and evaluating loops, save and load models, harness non-linearity, and evaluate classification models. He concludes by providing an example of how to numerically represent food photos and its prediction using a machine learning algorithm.
  
  
• 08:45:00 In this section, the instructor of the PyTorch for Deep Learning & Machine Learning course provides details on the numerical encoding process and the output format. The inputs to the machine learning algorithm are numerically encoded images, which have some associated outputs in prediction probabilities. The instructor notes that the closer the prediction probability is to one, the more confident the model is in its output. This output comes from looking at multiple samples, and it is possible to adjust the algorithm and data to improve these predictions. The encoded outputs must be changed to labels that are understandable to humans. Additionally, the instructor discusses the shape of tensors, including batch size, color channels, and height/width. A batch size of 32 is a common practice, and the shape can vary depending on the problem being solved.
  
  
• 08:50:00 In this section, the instructor explains the architecture of a classification model, which is the schematic of what a neural network is. The input layer shape is determined by the number of features, which have to be encoded as a numerical representation, and the output layer is often a prediction probability for a certain class. There are hyperparameters like the number of hidden layers, neurons per hidden layer, and output layer shape that must be decided by the user. The instructor also gives code examples for creating layers and neurons using PyTorch, and notes that the shapes will vary depending on the problem being solved.
  
  
• 08:55:00 In this section, the instructor discusses the components of a classification problem, including hidden layer activation, output activation, loss function, and optimizer, and provides examples of each. The instructor then introduces a multi-class classification problem and discusses how the architecture can be built to have multiple output features. Finally, the instructor transitions into writing code using PyTorch on Google CoLab, reminding the audience that all code will be saved on a GitHub repo. The instructor also emphasizes the importance of starting any machine learning problem with data.

Part 10

• 09:00:00 In this section, the video focuses on creating a custom dataset using the scikit-learn library. The make circles dataset is imported and 1000 samples are created with some added noise for randomness. The length of X and Y are printed, which indicate that there are 1000 samples of features and labels. The first five samples of X and Y are then printed, showcasing that the data is already numerical and only has two classes: zero and one for binary classification. A pandas data frame is then created, with the features labeled as X1 and X2, and random sampling is discussed as a potentially helpful approach for exploring large datasets.
  
  
• 09:05:00 In this section of the course, the instructor explains the toy dataset that will be used to practice building a neural network in PyTorch for binary classification. The dataset was generated using scikit-learn and consists of two circles with different colors representing the two classes of the binary classification problem. The instructor shows how data visualization can help to understand the dataset and prepare for building a neural network. The input and output shapes of the problem are also discussed, as well as how to split the dataset into training and test sets, which will be covered in the next section of the course.
  
  
• 09:10:00 In this section, the instructor discusses the importance of checking input and output shapes in machine learning, as they are common sources of errors. They demonstrate how to view the input and output shapes of a dataset using NumPy arrays and convert the data into PyTorch tensors. The process of converting data into tensors and splitting it into train and test sets is a crucial step in machine learning, even for toy datasets like the one used in this example. The instructor shows how to import PyTorch and ensure the version being used is 1.10, how to convert NumPy arrays to PyTorch tensors, and how to create train and test sets for the data.
  
  
• 09:15:00 In this section, the instructor demonstrates how to convert data from NumPy arrays into PyTorch's default type of float 32 using the command "torch.float". Failure to do so may result in errors later on. The instructor then shows how to split data into training and test sets using random split, which is done using scikit-learn's "train_test_split" function. The code example shows the order in which features and labels should appear while passing them into the function. The instructor also explains the use of the "test_size" parameter, where the value given is the percentage of data to be used as test data, and the "random_state" parameter that acts like a random seed.
  
  
• 09:20:00 In this section, the video covers splitting the data into training and testing sets by using the Scikit-learn library in PyTorch. The torch dot manual seed is set to make sure that the same random splits are used, ensuring they are the same as the ones you want to compare. By using the length of the train and test sets, the video explains that they have 800 and 200 samples, respectively, making up the data set that they will be working with. The next step is to create and pick a model to classify the red and blue dots. To accomplish this, they set up an agnostic code including the device such that it runs on an accelerator, construct the model, define loss, and use PyTorch to create a training and test loop that will be explored further in the next section.
  
  
• 09:25:00 In this section, we learn how to set up a GPU for PyTorch and create a device-agnostic code to ensure that the code will run on a CPU without any issues. We then move on to construct a model by subclassing an nn.Module and follow four main steps. Firstly, we create a model that subclasses an nn.Module. Secondly, we create two Linear Layers capable of handling the shapes of our data. Thirdly, we define a forward method that outlines the forward pass of the model. Fourthly, we instantiate the instance of our model class and send it to the target device. We learn that our model will be used to separate red and blue circles using a neural network.
  
  
• 09:30:00 In this section of the course, the instructor discusses how to define a neural network layer that is capable of handling input features. He goes on to explain that the number of features required for each layer depends on the data set being used. In this example, where X has two features, the first layer is defined as an "n-linear" with n features equal to two, while the second layer is defined with five features to help the model learn more patterns. The instructor also explains that the in-features of the second layer should match the out-features of the previous layer to avoid shape mismatch errors. Finally, he defines a Ford method that outlines the Ford pass and returns self layer two (which takes in self layer one and X as inputs).
  
  
• 09:35:00 In this section, the instructor explains how to instantiate an instance of the model class and send it to the target device. He shows how to create a simple multi-layer neural network, which he then demonstrates on the TensorFlow playground using two input features and passing them to a hidden layer with five neurons, which feeds into another layer that has one output feature. He fits the network to some data, and the test loss is approximately 50%, which means if the model was just randomly guessing, it would get a loss of about 0.5, because there are only two classes.
  
  
• 09:40:00 In this section of the video, the instructor uses a whiteboard tool called Fig Jam to visually represent a neural network for a binary classification problem. The instructor explains that in a binary classification problem, randomly guessing will get you around 50% accuracy. The neural network is constructed using inputs, hidden units, and an output layer, and the instructor emphasizes that the shape of the layers must match. The TensorFlow playground is suggested as a fun way to explore and challenge oneself in building a neural network on this type of data. Later on, the instructor discusses replicating the previously created neural network with even less code using two linear layers capable of handling the input features and upscaling them to improve the network's learning.
  
  
• 09:45:00 In this section, the instructor demonstrates how to replicate a neural network model using nn.Sequential in PyTorch. By using nn.Sequential, the code for the model can be simplified, as most of the code is implemented behind the scenes. The instructor explains that using nn.Sequential for simple, straightforward operations can be more efficient than subclassing, as demonstrated in a previous section of the video. However, subclassing allows for more complex operations, such as building more complex forward passes. This section highlights the flexibility of PyTorch and the different ways to make a model. The instructor also demonstrates passing data through the model and analyzing the state dictionary.
  
  
• 09:50:00 In this section, the instructor demonstrates how PyTorch automatically creates weight and bias parameters behind the scenes while implementing a two-layer neural network. The instructor highlights the fact that the model is instantiated with random numbers and that PyTorch will change these values slightly during the backpropagation and gradient descent process to better fit or represent the data. The instructor also shows the potential complexity of having many layers with numerous features and how keeping track of these values by hand can become verbose. Finally, the instructor goes on to make predictions using the untrained model and highlights the importance of troubleshooting and visualizing the data.
  
  
• 09:55:00 In this section, the video explains how to pick a loss function and optimizer after creating a model for deep learning. The type of loss function and optimizer needed usually depends on the nature of the dataset being worked on. For regression problems, the mean absolute error or mean squared error might be appropriate. Meanwhile, for classification issues, binary cross-entropy or categorical cross-entropy could be chosen. The video concludes by noting that the loss function helps to measure how accurate a model's predictions are.