![Neural networks made easy (Part 25): Practicing Transfer Learning](https://c.mql5.com/2/49/Neural_Networks_Easy_017_600x314.jpg)
Neural networks made easy (Part 25): Practicing Transfer Learning
In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.
![Data Science and Machine Learning (Part 06): Gradient Descent](https://c.mql5.com/2/49/gradient_descent_600x314.jpg)
Data Science and Machine Learning (Part 06): Gradient Descent
The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.
![Experiments with neural networks (Part 2): Smart neural network optimization](https://c.mql5.com/2/51/neural_network_experiments_p2_600x314.jpg)
Experiments with neural networks (Part 2): Smart neural network optimization
In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading.
![Neural networks made easy (Part 31): Evolutionary algorithms](https://c.mql5.com/2/50/Neural_Networks_are_Simple-_Part_31_600x314.jpg)
Neural networks made easy (Part 31): Evolutionary algorithms
In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.
![Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)](https://c.mql5.com/2/57/cic-055_600x314.jpg)
Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)
Contrastive training is an unsupervised method of training representation. Its goal is to train a model to highlight similarities and differences in data sets. In this article, we will talk about using contrastive training approaches to explore different Actor skills.
![How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 4): Triangular moving average — Indicator Signals](https://c.mql5.com/2/60/rj-article-images_600x314.jpg)
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 4): Triangular moving average — Indicator Signals
The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders for example: Trailing Stop Loss and Trailing Profit) for more than one symbol pair only from one symbol chart. This time we will use only 1 indicator, namely Triangular moving average in multi-timeframes or single timeframe.
![Developing a trading Expert Advisor from scratch (Part 11): Cross order system](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_002_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 11): Cross order system
In this article we will create a system of cross orders. There is one type of assets that makes traders' life very difficult for traders — futures contracts. But why do they make life difficult?
![Neural networks made easy (Part 33): Quantile regression in distributed Q-learning](https://c.mql5.com/2/50/Neural_Networks_Made_Easy_q-learning_600x314.jpg)
Neural networks made easy (Part 33): Quantile regression in distributed Q-learning
We continue studying distributed Q-learning. Today we will look at this approach from the other side. We will consider the possibility of using quantile regression to solve price prediction tasks.
![How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session](https://c.mql5.com/2/60/Parabolic_SAR_MTF_600x314.jpg)
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session
Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.
![Neural networks made easy (Part 56): Using nuclear norm to drive research](https://c.mql5.com/2/57/nuclear_norm_utilization_600x314.jpg)
Neural networks made easy (Part 56): Using nuclear norm to drive research
The study of the environment in reinforcement learning is a pressing problem. We have already looked at some approaches previously. In this article, we will have a look at yet another method based on maximizing the nuclear norm. It allows agents to identify environmental states with a high degree of novelty and diversity.
![Data Science and Machine Learning(Part 21): Unlocking Neural Networks, Optimization algorithms demystified](https://c.mql5.com/2/73/Data_Science_and_Machine_Learning_Part_21_600x314.jpg)
Data Science and Machine Learning(Part 21): Unlocking Neural Networks, Optimization algorithms demystified
Dive into the heart of neural networks as we demystify the optimization algorithms used inside the neural network. In this article, discover the key techniques that unlock the full potential of neural networks, propelling your models to new heights of accuracy and efficiency.
![Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)](https://c.mql5.com/2/57/random_encoder_for_efficient_exploration_054_600x314.jpg)
Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)
Whenever we consider reinforcement learning methods, we are faced with the issue of efficiently exploring the environment. Solving this issue often leads to complication of the algorithm and training of additional models. In this article, we will look at an alternative approach to solving this problem.
![Experiments with neural networks (Part 3): Practical application](https://c.mql5.com/2/51/neural_network_experiments_p3_600x314.jpg)
Experiments with neural networks (Part 3): Practical application
In this article series, I use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 is approached as a self-sufficient tool for using neural networks in trading.
![Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_005_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)
Today we will add some more resources to our EA. This interesting article can provide some new ideas and methods of presenting information. At the same time, it can assist in fixing minor flaws in your projects.
![Neural networks made easy (Part 37): Sparse Attention](https://c.mql5.com/2/53/NN_part_37_Sparse_Attention_600x314.jpg)
Neural networks made easy (Part 37): Sparse Attention
In the previous article, we discussed relational models which use attention mechanisms in their architecture. One of the specific features of these models is the intensive utilization of computing resources. In this article, we will consider one of the mechanisms for reducing the number of computational operations inside the Self-Attention block. This will increase the general performance of the model.
![Developing a trading Expert Advisor from scratch (Part 20): New order system (III)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_011_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 20): New order system (III)
We continue to implement the new order system. The creation of such a system requires a good command of MQL5, as well as an understanding of how the MetaTrader 5 platform actually works and what resources it provides.
![Category Theory in MQL5 (Part 8): Monoids](https://c.mql5.com/2/54/Category-Theory-p8_600x314.jpg)
Category Theory in MQL5 (Part 8): Monoids
This article continues the series on category theory implementation in MQL5. Here we introduce monoids as domain (set) that sets category theory apart from other data classification methods by including rules and an identity element.
![Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_008_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)
In this article we continue considering how to obtain data from the web and to use it in an Expert Advisor. This time we will proceed to developing an alternative system.
![Data label for time series mining (Part 3):Example for using label data](https://c.mql5.com/2/58/Data_label_for_time_series_mining_V4_Impr_600x314.jpg)
Data label for time series mining (Part 3):Example for using label data
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Improve Your Trading Charts With Interactive GUI's in MQL5 (Part II): Movable GUI (II)](https://c.mql5.com/2/56/Revolutionize_Your_Trading_Charts_Part_2_600x314.jpg)
Improve Your Trading Charts With Interactive GUI's in MQL5 (Part II): Movable GUI (II)
Unlock the potential of dynamic data representation in your trading strategies and utilities with our in-depth guide to creating movable GUIs in MQL5. Delve into the fundamental principles of object-oriented programming and discover how to design and implement single or multiple movable GUIs on the same chart with ease and efficiency.
![Improve Your Trading Charts With Interactive GUI's in MQL5 (Part I): Movable GUI (I)](https://c.mql5.com/2/55/Revolutionize_Your_Trading_Charts_Part_I_600x314.jpg)
Improve Your Trading Charts With Interactive GUI's in MQL5 (Part I): Movable GUI (I)
Unleash the power of dynamic data representation in your trading strategies or utilities with our comprehensive guide on creating movable GUI in MQL5. Dive into the core concept of chart events and learn how to design and implement simple and multiple movable GUI on the same chart. This article also explores the process of adding elements to your GUI, enhancing their functionality and aesthetic appeal.
![Outline of MetaTrader Market (Infographics)](https://c.mql5.com/2/10/infographic_market_av__1.png)
![Outline of MetaTrader Market (Infographics)](https://c.mql5.com/i/articles/overlay.png)
Outline of MetaTrader Market (Infographics)
A few weeks ago we published the infographic on Freelance service. We also promised to reveal some statistics of the MetaTrader Market. Now, we invite you to examine the data we have gathered.
![MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves](https://c.mql5.com/2/62/midjourney_image_13915_50_439_5_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves
K-Means clustering takes the approach to grouping data points as a process that’s initially focused on the macro view of a data set that uses random generated cluster centroids before zooming in and adjusting these centroids to accurately represent the data set. We will look at this and exploit a few of its use cases.
![Creating multi-symbol, multi-period indicators](https://c.mql5.com/2/59/multi-period_indicators_4_600x314.jpg)
Creating multi-symbol, multi-period indicators
In this article, we will look at the principles of creating multi-symbol, multi-period indicators. We will also see how to access the data of such indicators from Expert Advisors and other indicators. We will consider the main features of using multi-indicators in Expert Advisors and indicators and will see how to plot them through custom indicator buffers.
![Creating an EA that works automatically (Part 07): Account types (II)](https://c.mql5.com/2/50/aprendendo_construindo_007_600x314.jpg)
Creating an EA that works automatically (Part 07): Account types (II)
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. The trader should always be aware of what the automatic EA is doing, so that if it "goes off the rails", the trader could remove it from the chart as soon as possible and take control of the situation.
![Neural networks made easy (Part 18): Association rules](https://c.mql5.com/2/49/Neural_Networks_Easy_010_600x314.jpg)
Neural networks made easy (Part 18): Association rules
As a continuation of this series of articles, let's consider another type of problems within unsupervised learning methods: mining association rules. This problem type was first used in retail, namely supermarkets, to analyze market baskets. In this article, we will talk about the applicability of such algorithms in trading.
![Design Patterns in software development and MQL5 (Part 3): Behavioral Patterns 1](https://c.mql5.com/2/61/Design_Patterns_wPart_3z_Behavioral_Patterns_1_600x314.jpg)
Design Patterns in software development and MQL5 (Part 3): Behavioral Patterns 1
A new article from Design Patterns articles and we will take a look at one of its types which is behavioral patterns to understand how we can build communication methods between created objects effectively. By completing these Behavior patterns we will be able to understand how we can create and build a reusable, extendable, tested software.
![Introduction to MQL5 (Part 7): Beginner's Guide to Building Expert Advisors and Utilizing AI-Generated Code in MQL5](https://c.mql5.com/2/77/Introduction_to_MQL5_sPart_7v_Beginnerys_Guide_to_Building_Expert_Advisors_and_Utilizing_AI-Generate.jpg)
Introduction to MQL5 (Part 7): Beginner's Guide to Building Expert Advisors and Utilizing AI-Generated Code in MQL5
Discover the ultimate beginner's guide to building Expert Advisors (EAs) with MQL5 in our comprehensive article. Learn step-by-step how to construct EAs using pseudocode and harness the power of AI-generated code. Whether you're new to algorithmic trading or seeking to enhance your skills, this guide provides a clear path to creating effective EAs.
![Developing a trading Expert Advisor from scratch (Part 24): Providing system robustness (I)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_003_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 24): Providing system robustness (I)
In this article, we will make the system more reliable to ensure a robust and secure use. One of the ways to achieve the desired robustness is to try to re-use the code as much as possible so that it is constantly tested in different cases. But this is only one of the ways. Another one is to use OOP.
![Neural networks made easy (Part 17): Dimensionality reduction](https://c.mql5.com/2/49/Neural_networks_made_easy_007_600x314.jpg)
Neural networks made easy (Part 17): Dimensionality reduction
In this part we continue discussing Artificial Intelligence models. Namely, we study unsupervised learning algorithms. We have already discussed one of the clustering algorithms. In this article, I am sharing a variant of solving problems related to dimensionality reduction.
![Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models](https://c.mql5.com/2/70/Deep_Learning_GRU_model_with_Python_to_ONNX_with_EAh_and_GRU_vs_LSTM_models_600x314.jpg)
Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models
We will guide you through the entire process of DL with python to make a GRU ONNX model, culminating in the creation of an Expert Advisor (EA) designed for trading, and subsequently comparing GRU model with LSTN model.
![Neural networks made easy (Part 58): Decision Transformer (DT)](https://c.mql5.com/2/58/decision-transformer_600x314.jpg)
Neural networks made easy (Part 58): Decision Transformer (DT)
We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.
![Testing and optimization of binary options strategies in MetaTrader 5](https://c.mql5.com/2/0/binary-strategy-tester_600x314.jpg)
Testing and optimization of binary options strategies in MetaTrader 5
In this article, I will check and optimize binary options strategies in MetaTrader 5.
![Building Your First Glass-box Model Using Python And MQL5](https://c.mql5.com/2/61/Building_Your_First_Glass_Box_Model_Using_Python_And_MQL5_600x314.jpg)
Building Your First Glass-box Model Using Python And MQL5
Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.
![Neural networks made easy (Part 53): Reward decomposition](https://c.mql5.com/2/57/decomposition_of_remuneration_053_600x314.jpg)
Neural networks made easy (Part 53): Reward decomposition
We have already talked more than once about the importance of correctly selecting the reward function, which we use to stimulate the desired behavior of the Agent by adding rewards or penalties for individual actions. But the question remains open about the decryption of our signals by the Agent. In this article, we will talk about reward decomposition in terms of transmitting individual signals to the trained Agent.
![Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor](https://c.mql5.com/2/71/MQL5_Article-02_Artwork_hero_1200_x_628px_600x314.jpg)
Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor
Learn about the object-oriented programming paradigm and its application in MQL5 code. This second article goes deeper into the specifics of object-oriented programming, offering hands-on experience through a practical example. You'll learn how to convert our earlier developed procedural price action expert advisor using the EMA indicator and candlestick price data to object-oriented code.
![Neural networks made easy (Part 22): Unsupervised learning of recurrent models](https://c.mql5.com/2/49/Neural_Networks_Easy_014_600x314.jpg)
Neural networks made easy (Part 22): Unsupervised learning of recurrent models
We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.
![Data label for timeseries mining (Part 2):Make datasets with trend markers using Python](https://c.mql5.com/2/58/Make_datasets_with_trend_markers_using_Python_600x314.jpg)
Data label for timeseries mining (Part 2):Make datasets with trend markers using Python
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps](https://c.mql5.com/2/52/data_science_ml_kohonen_maps_014_600x314.jpg)
Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps
Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
![Neural networks made easy (Part 44): Learning skills with dynamics in mind](https://c.mql5.com/2/55/Neural_Networks_are_Just_a_Part_600x314.jpg)
Neural networks made easy (Part 44): Learning skills with dynamics in mind
In the previous article, we introduced the DIAYN method, which offers the algorithm for learning a variety of skills. The acquired skills can be used for various tasks. But such skills can be quite unpredictable, which can make them difficult to use. In this article, we will look at an algorithm for learning predictable skills.