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Cool, I'm excited to read this series.

Was a little disappointed to see neural networks noted as "classical" with SVMs designated "modern". And nothing about deep learning? Autoencoders? How about different optimization methods--Truncated newton vs gradient descent?

Some of the most interesting recent developments in ML seem to be left out, even if it is just an introduction.



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