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  1. Data Science and Machine Learning (Part 09) : The K-Nearest Neighbors Algorithm (KNN)

    by , 11-19-2022 at 12:07 AM
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    K-Nearest Neighbors Algorithm is a non-parametric supervised learning classifier that uses proximity to make classifications or predictions about the grouping of an individual data point. While this algorithm is mostly used for classification problems, It can be used for solving a regression problem too, It is often used as a classification algorithm due to its assumption that similar points in the dataset can be found near one another; k-nearest neighbors
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  2. Neural networks made easy (Part 27): Deep Q-Learning (DQN)

    by , 11-14-2022 at 12:39 AM
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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.
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  3. Neural networks made easy (Part 26): Reinforcement Learning

    by , 11-13-2022 at 12:31 AM
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    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.
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  4. How to deal with lines using MQL5

    by , 11-11-2022 at 04:08 AM
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    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).
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  5. Neural networks made easy (Part 25): Practicing Transfer Learning

    by , 11-06-2022 at 01:17 AM
    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.
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    1. Neural networks made easy
    2. Neural networks made easy (Part 2): Network training and testing
    3. Neural networks made easy (Part 3): Convolutional networks
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