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  1. Data Science and Machine Learning (Part 06): Gradient Descent

    by , 07-31-2022 at 01:53 AM
    The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a local maximum of that function; the procedure is then known as gradient ascent.
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  2. Neural networks made easy (Part 16): Practical use of clustering

    by , 07-30-2022 at 01:48 AM
    The previous two articles were devoted to data clustering. But our main goal is to learn how to use all the considered methods to solve specific practical problems. In particular, trading related cases. When we started considering unsupervised learning methods, we talked about the possibility of using the results obtained both independently and as input data for other models. In this article, we will consider possible use cases for clustering results.[/QUOTE]
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  3. Developing a trading Expert Advisor from scratch (Part 16): Accessing data on the web (II)

    by , 07-28-2022 at 01:24 AM
    In the previous article "Developing a trading Expert Advisor from scratch (Part 15): Accessing data on the web (I)", we presented the entire logic and ideas behind the methods of using the MetaTrader 5 platform to access marked data from specialized websites.
    In that article, we considered how to access these sites and how to find and retrieve information from them in order to use it in the platform. But it doesn't end there, as simply capturing data doesn't make much sense. The most
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  4. The price movement model and its main provisions (Part 1): The simplest model version and its applications

    by , 07-24-2022 at 09:17 PM
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    Up to the present, we have not had any mathematically rigorous price movement theory. Instead, we have had to deal with experience-based assumptions stating that the price moves in a certain way after a certain pattern. Of course, these assumptions have been supported neither by statistics, nor by theory. The article provides the concepts and foundations of the mathematical apparatus of the rigorous theory price movement theory.
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  5. Neural networks made easy (Part 15): Data clustering using MQL5

    by , 07-24-2022 at 01:30 AM
    In the previous article, we considered the k-means clustering method and studied its implementation using the Python language. However, the use of integration often imposes certain restrictions and additional costs. In particular, the current integration state does not allow the use of data of built-in applications such as indicators or terminal event handling. A lot of classic indicators are implemented in various libraries, but when we talk about custom indicators, we will need to reproduce
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