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  1. Neural networks made easy (Part 38): Self-Supervised Exploration via Disagreement

    by , 03-14-2024 at 05:18 AM
    This algorithm is based on a self-learning method, where the agent uses information obtained during interaction with the environment to generate "intrinsic" rewards and update its strategy. The algorithm is based on the use of several agent models that interact with the environment and generate various predictions. If the models disagree, it is considered an "interesting" event and the agent is incentivized to explore that space of the environment. In this way, the algorithm incentivizes the agent
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  2. Evaluating ONNX models using regression metrics

    by , 03-06-2024 at 05:19 AM
    Regression is a task of predicting a real value from an unlabeled example. A well-known example of regression is estimating the value of a diamond based on such characteristics as size, weight, color, clarity, etc.

    The so-called regression metrics are used to assess the accuracy of regression model predictions. Despite similar algorithms, regression metrics are semantically different from similar loss functions.

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  3. Neural networks made easy (Part 37): Sparse Attention

    by , 02-26-2024 at 05:19 AM
    In the previous article, we discussed relational models which use attention mechanisms in their architecture. We used this model to create an Expert Advisor, and the resulting EA showed good results. However, we noticed that the model's learning rate was lower compared to our earlier experiments. This is due to the fact that the transformer block used in the model is a rather complex architectural solution performing a large number of operations. The number of these operations grows in a quadratic
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  4. Data label for time series mining(Part 1)

    by , 02-19-2024 at 05:19 AM
    When we design artificial intelligence models, we often need to prepare data first. Good data quality will allow us to get twice the result with half the effort in model training and validation. But our foreign exchange or stock data is special, which contains complex market information and time information, and data labeling is difficult, but we can easily analyze the trend in historical data on the chart.

    This section introduces a method of making data sets with trend marks by EA
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  5. Wrapping ONNX models in classes

    by , 02-10-2024 at 05:19 AM
    In the previous article, we used two ONNX models to arrange the voting classifier. The entire source text was organized as a single MQ5 file. The entire code was divided into functions. But what if we try to swap models? Or add another model? The original text will become even bigger. Let's try the object-oriented approach.
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