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  1. Neural networks made easy (Part 24): Improving the tool for Transfer Learning

    by , 10-30-2022 at 10:20 PM
    In the previous article in this series, we have created a tool to take advantage of the Transfer Learning technology. As a result of the work done, we got a tool that allows the editing of already trained models.

    Furthermore, the created tool allows not only editing trained models. It also allows creating completely new ones.

    Such a useful toll should also be as user friendly as possible. Thus, in this article, we will try to improve its usability.
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  2. Learn how to design a trading system by Fractals

    by , 10-28-2022 at 02:48 AM
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    A new article with a new technical indicator in our series as we will learn how to design a trading system based on one of the most popular technical indicators that's the Fractals indicator.
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  3. Market math: profit, loss and costs

    by , 10-26-2022 at 02:27 AM
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    After reading this article, you will receive a complete and working mathematical model, as well as learn to understand and correctly calculate everything related to orders.
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  4. Population optimization algorithms

    by , 10-21-2022 at 02:52 AM
    When optimizing trading systems, the most exciting things are metaheuristic optimization algorithms. They do not require knowledge of the formula of the function being optimized. Population algorithms involve the simultaneous handling of several options for solving the optimization problem and represent an alternative to classical algorithms based on motion trajectories whose search area has only one candidate evolving when solving the problem.
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  5. Neural networks made easy (Part 23): Building a tool for Transfer Learning

    by , 10-19-2022 at 10:07 PM
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    We continue our immersion in the world of artificial intelligence. What is Transfer Learning and why do we need it? Transfer Learning is a machine learning method in which the knowledge of a model trained to solve one problem is reused as a basis for solving new problems. Of course, to solve new problems, the model is preliminarily additionally trained on new data. In the general case, with a properly selected donor model, additional training runs much
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