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  1. Neural networks made easy (Part 30): Genetic algorithms

    by , 12-07-2022 at 12:36 AM
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    We continue to study model training algorithms. All the previously considered algorithms used an analytical method for determining the direction and strength of changes in model parameters during the learning process. While the methods to solve these problems turn to be inefficient. In such cases, we resort to evolutionary optimization methods.
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    1. Neural networks made easy
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  2. Neural networks made easy (Part 29): Advantage Actor-Critic algorithm

    by , 12-02-2022 at 12:12 AM
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    We continue to explore reinforcement learning methods. In previous articles we discussed methods for approximating the Q-learning Reward function and the policy gradient function learning. Each method has its own advantages and disadvantages. It would be great to use the maximum of their advantages when building and training models. When trying to find methods minimizing the shortcomings of the algorithms used, we often try to build certain conglomerates
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