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  1. The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI

    by , 04-25-2024 at 03:05 PM
    The disagreement is an open area of research in an interdisciplinary field known as Explainable Artificial Intelligence (XAI). Explainable Artificial Intelligence attempts to help us understand how our models are arriving at their decisions but unfortunately everything is easier said than done.

    We are all aware that machine learning models and available datasets are growing larger and more complex. As a matter of fact, the data scientists who develop machine learning algorithms cannot
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  2. Neural networks made easy (Part 37): Sparse Attention

    by , 04-20-2024 at 03:24 PM
    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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  3. Mastering Model Interpretation: Gaining Deeper Insight From Your Machine Learning Models

    by , 04-12-2024 at 06:31 AM
    In the realm of machine learning, more often than not we think in terms of trade offs. While optimising one metric of performance, we often compromise another performance metric. With the growing evolutionary trend of increasingly larger and more intricate models, understanding, explaining and debugging them become formidable tasks. The intricacies beneath the model's surface, deciphering 'why' our models are making the decisions they are making is vital. Without this clarity how can we confidently
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  4. Quantization in machine learning (Part 2): Data preprocessing, table selection, training CatBoost models

    by , 04-07-2024 at 04:11 PM
    The article considers the practical application of quantization in the construction of tree models No complex mathematical equations are used. This is the second part of the article "Quantization and other methods of preprocessing input data in machine learning", so I strongly recommend starting your acquaintance with it. Here we will talk about the following:

    • In the first part, we will consider the methods for preprocessing sample data implemented in MQL5.
    • In the second
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  5. Evaluating ONNX models using regression metrics

    by , 04-06-2024 at 03:24 PM
    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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