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If you are looking for a book that is more beginner friendly than the Sutton and Barto book (which you should of course check out also), try out: Deep Reinforcement Learning Hands On


Reinforcement Learning: An Introduction by Richard Sutton and Andrew Barto is undoubtedly one of the best books, to begin with. Despite its age, the book is still the canonical introduction to reinforcement learning. It does require some patience, but I think it's very approachable and rigorous at the same time!


I think you are looking for the field known as explainable artificial intelligence. The book Interpretable Machine Learning: A Guide for Making Black Box Models Explainable will surely help you to understand the issues and existing techniques. See also the following question Which explainable artificial intelligence techniques are there?.


I'm currently working with Temporal Convolution Networks (TCNs) for making predictions with time series data (link to article here: These types of networks, like other types of convolutional networks for time series, use a dilated convolution operation, which, unlike the standard ...


Just wanted to add that the new text Deep Learning Architectures A Mathematical Approach mentions this result, but I'm not sure if it gives a proof. It does mention an improved result by Hanin ( for which I think it does give at least a partial proof. The original paper by Hanin seems to omit some proofs as well, but the ...

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