You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Gail Weiss: Thinking Like Transformers
Gail Weiss: Thinking Like Transformers
Gail Weiss discusses the concept of transformer encoders in this video, explaining their ability to process sequences and encode them into vectors. Weiss highlights several studies exploring the strengths and limitations of transformer encoders and introduces a programming language called the restricted access sequence processing language (RASP) to represent transformer encoders abilities. She also discusses multi-headed attention, selection patterns, and the challenges of softmax under certain conditions, before delving into the use of sequence operators and library functions to compute the inverse and the flip selector. Weiss provides insight into creating an optimal program for a transformer and the insights from the Universal and Sandwich Transformers, ultimately discussing the select predicate and binary vs. order three relations.
He also talks about the potential benefits and drawbacks of using higher order attention in transformer models, as well as the importance of residual connections in maintaining information throughout the layers. She also discusses potential issues with very deep transformers deviating from the RASP model and suggests the use of longer embeddings to overcome fuzziness in information.
Visualizing and Understanding Deep Neural Networks by Matt Zeiler
Visualizing and Understanding Deep Neural Networks by Matt Zeiler
Matt Zeiler discusses visualizing and understanding convolutional neural networks (CNNs) for object recognition in images and videos. He describes how deep neural networks perform compared to humans and primates in recognizing objects and shows how CNNs learn to identify objects by going through the layers. Zeiler explains the process of improving CNN architecture and discusses the limitations when training with limited data. Lastly, he answers questions about using lower layers in higher layers and the application of convolutions in neural networks.
How ChatGPT is Trained
How ChatGPT is Trained
ChatGPT is a machine learning system that is designed to mimic human conversation. It is first trained using a generative pre-training approach that relies on massive amounts of unstructured text data, and then fine-tuned using reinforcement learning to better adapt to the user's preferences.
The REAL potential of generative AI
The REAL potential of generative AI
Generative AI has the potential to revolutionize the way products are created, by helping developers with prototyping, evaluation, and customization. However, the technology is still in its early stages, and more research is needed to ensure that it is used ethically and safely.
Vrije Universiteit Amsterdam Machine Learning 2019 - 1 Introduction to Machine Learning (MLVU2019)
Vrije Universiteit Amsterdam Machine Learning 2019 - 1 Introduction to Machine Learning (MLVU2019)
This video provides an introduction to machine learning and covers various topics related to it. The instructor explains how to prepare for the course and addresses common concerns about machine learning being intimidating. He introduces the different types of machine learning and distinguishes it from traditional rule-based programming. The video also covers the basics of supervised learning and provides examples of how machine learning can be used for classification and regression problems. The concepts of feature space, loss function, and residuals are explained as well.
The second part of the video provides an introduction to machine learning and explains its main goal of finding patterns and creating accurate models to predict outcomes from a dataset. The speaker discusses the importance of using specific algorithms and data splitting to avoid overfitting and achieve generalization. He also introduces the concept of density estimation and its difficulties with complex data. The speaker clarifies the difference between machine learning and other fields and alludes to a strategy for breaking down big data sets in order to make accurate predictions. The video also mentions the increase of people working in machine learning with the development of deep learning and provides tips for beginners to get started in the field.
2 Linear Models 1: Hyperplanes, Random Search, Gradient Descent (MLVU2019)
2 Linear Models 1: Hyperplanes, Random Search, Gradient Descent (MLVU2019)
This video covers the basics of linear models, search methods, and optimization algorithms. Linear models are explained in both 2 dimensions and multiple dimensions, and the process of searching for a good model through methods such as random search and gradient descent is discussed. The importance of convexity in machine learning is explained, and the drawbacks of random search in non-convex landscapes are addressed. The video also introduces evolutionary methods and branching search as search methods. Finally, the use of calculus and gradient descent to optimize the loss function is explained, including the process of finding the direction of steepest descent for a hyperplane.
The second part discusses gradient descent and its application to linear models, where the algorithm updates the parameters by taking steps in the direction of the negative gradient of the loss function. The learning rate is crucial in determining how quickly the algorithm converges to the minimum, and linear functions allow one to work out the optimal model without having to search. However, more complex models require using gradient descent. The video also introduces classification and decision boundaries, where the goal is to separate blue points from red points by finding a line that does so optimally. Limitations of linear models include their inability to classify non-linearly separable datasets, but they are computationally cheap and work well in high dimensional feature spaces. The instructor also previews future topics that will be discussed, such as machine learning methodology.
3 Methodology 1: Area-under-the-curve, bias and variance, no free lunch (MLVU2019)
3 Methodology 1: Area-under-the-curve, bias and variance, no free lunch (MLVU2019)
The video covers the use of the area-under-the-curve (AUC) metric in evaluating machine learning models, as well as introducing the concepts of bias and variance, and the "no free lunch" theorem. The AUC metric measures the classification model's performance by calculating the area under the ROC curve. Additionally, bias and variance are discussed as they play a crucial role in how well the model fits the training data and generalizes to new data. Also, the "no free lunch" theorem highlights the need to select the appropriate algorithm for each specific problem since there is no universally applicable algorithm for all machine learning problems.
This video covers three important machine learning concepts: AUC (area-under-the-curve), bias and variance, and the "no free lunch" theorem. AUC is a metric used to evaluate binary classification models, while bias and variance refer to differences between a model's predicted values and the true values in a dataset. The "no free lunch" theorem highlights the importance of selecting the appropriate algorithm for a given problem, as there is no single algorithm that can perform optimally on all possible problems and datasets.
4 Methodology 2: Data cleaning, Principal Component Analysis, Eigenfaces (MLVU2019)
4 Methodology 2: Data cleaning, Principal Component Analysis, Eigenfaces (MLVU2019)
This first part of the video covers various important aspects of data pre-processing and cleaning before applying machine learning algorithms, starting with the crucial importance of understanding data biases and skew. The speaker then discusses methods for dealing with missing data, outliers, class imbalance, feature selection, and normalization. The video goes on to discuss the concept of basis and the MVN distribution, explaining how to use whitening to transform data into a normal distribution for normalization, and concludes with the use of principal component analysis (PCA) for dimensionality reduction. From manipulating the training set to using imputation methods, PCA projects data down to a lower dimensional space while retaining information from the original data.
This second part of the video discusses the use of Principal Component Analysis (PCA) in data cleaning and dimensionality reduction for machine learning. The method involves mean centering the data, computing the sample covariance, and decomposing it using eigen decomposition to obtain the eigenvectors aligned with the axis that capture the most variance. Using the first K principal components provides a good data reconstruction, allowing for better machine learning performance. The concept of Eigenfaces is also introduced, and PCA is shown to be effective in compressing the data to 30 dimensions while maintaining most of the required information for machine learning. Various applications of PCA are discussed, including its use in anthropology and in the study of complex datasets such as DNA and faces.
Lecture 5 Probability 1: Entropy, (Naive) Bayes, Cross-entropy loss (MLVU2019)
5 Probability 1: Entropy, (Naive) Bayes, Cross-entropy loss (MLVU2019)
The video covers various aspects of probability theory and its application in machine learning. The speaker introduces entropy, which measures the amount of uncertainty in a system, and explains how it is related to naive Bayes and cross-entropy loss. The concepts of sample space, event space, random variables, and conditional probability are also discussed. Bayes' theorem is explained and considered a fundamental concept in machine learning. The video also covers maximum likelihood estimation principle and Bayesian probability, as well as the use of prefix-free code to simulate probability distributions. Lastly, the speaker discusses discriminative versus generative classifiers for binary classification, including the Naive Bayes classifier.
The second part explains the concept of computing probabilities for a new point belonging to a particular class using a multivariate normal distribution model. It discusses conditional independence of features to efficiently fit probability distributions for a classifier, and the need for smoothing or tuning pseudo-observations to handle zero instances. The speaker also introduces entropy loss as a more effective loss function for linear classifiers than accuracy, and discusses the cross-entropy loss function's ability to measure the difference between predicted and actual data, with the sigmoid function collapsing the function's symmetries to simplify it. Finally, the video hints that the next lecture will cover SVM loss as the final loss function.
Lecture 6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019)
6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019)
This first part of the video on linear models focuses on introducing non-linearity to linear models and explores two models that rely on expanding the feature space: neural networks and support vector machines (SVMs). For neural networks, the speaker explains how to set up a network for regression and classification problems using activation functions such as sigmoid or softmax. The lecture then delves into backpropagation, a method used to compute gradients used in neural networks. For SVMs, the speaker introduces the concept of maximizing the margin to the nearest points of each class and demonstrates how it can be expressed as a constrained optimization problem. The video provides a clear introduction to the principles of neural networks and SVMs, recommending students focus on the first half of the lecture as a starting point for the rest of the course.
The second part of the video covers the topics of support vector machines (SVMs), soft margin SVMs, kernel tricks, and differences between SVMs and neural networks. The soft margin SVMs are introduced as a way to handle non-linearly separable data, allowing for a penalty value to be added to points that do not comply with classification constraints. The kernel trick allows for the computation of the dot product in a higher-dimensional space, expanding the feature space to significantly increase the model's power. The differences between SVMs and neural networks are explained, and the shift towards neural networks due to their ability to perform more advanced types of classification, even if not fully understood, is discussed.