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I wonder if there is some course that would cover a bit more advanced topics in a comprehensible manner. There are hundreds of courses/books/tutorials that cover pretty much the same stuff again and again.

General ML: supervised vs unsupervised, K-means clustering, linear regression, logistic regression, maybe several enseble learning methods based on trees.

NNs: backpropagation, gradient descent, tensorflow, a bit about meta-param selection, CNNs (basically, just ImageNet), sometimes RNNs are mentioned.

This is all pretty entry-level and covered many times over, but, surprisingly, that's pretty much it. Discussion of models pretty much stops at ImageNet. I rarely see RBM or autoencoder, and pretty much nothing about how real problems are encoded into inputs and outputs.

I am ashamed to admit, but I still don't really understand how AlphaZero, AlphaStar or various language models (GPT, BERT) really work. Is there something good on that, maybe?




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