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In the previous article, we started exploring reinforcement learning methods and built our first cross-entropy trainable model. In this article, we continue to study reinforcement learning methods. We will proceed to the Deep Q-Learning method. more...
In the previous articles of this series, we have already seen supervised and unsupervised learning algorithms. This article opens another machine learning chapter: Reinforcement Learning. more...
We all as traders draw many lines while trading to help us to observe some important levels to take a suitable trading decision based on them. So, these lines are very important for us as traders and we may wonder if we have a method that can be used to draw these lines or take a suitable decision based on them automatically because I think that it will help us a lot. The answer is yes, we have a method to do that by MQL5 (MetaQuotes Language 5). ...
We continue to study the Transfer Learning technology. In the previous two articles, we created a tool for creating and editing neural network models. This tool will help us transfer part of the pre-trained model to a new model and supplement it with new decision layers. We will also check the usability of our tool. more... --------------------- Neural networks made easyNeural networks made easy (Part 2): Network training and testingNeural networks made easy (Part 3): Convolutional networks ...
Is a Machine learning paradigm for problems where the available data consists of unlabeled examples. Unlike supervised learning techniques such as regression methods, SVM, decision trees, neural networks, and many others discussed in this article series, where we always have labeled datasets that we fit our models upon. In unsupervised learning, the data is unlabeled so, it's up to the algorithm to figure out the relationship and everything else on itself. ...
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