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How to teach yourself ML in two easy steps 1) Have a strong knowledge of undergraduate mathematics, probability, statistics, numerics 2) read a book about mac
by internet555 8y ago
How to teach yourself ML in two easy steps
1) Have a strong knowledge of undergraduate mathematics, probability, statistics, numerics
2) read a book about machine learning
- stochastic_monk 8y agoPiggybacking on this, I instead recommend an introductory ML book like Bishop or Murphy, a statistical ML book like Mohri or Shai Shalev-Schwartz, and a textbook on nonlinear optimization, convex or otherwise. The jump from classical machine learning to deep learning is not far if you have a good understanding of first principles.
- basjacobs 8y agoAny reason to pick Mohri over Shai Shalev-Schwartz, or the other way around?
- stochastic_monk 8y agoI think that SSS is a little easier to follow, while Mohri is more complete. It was helpful to me to have both, as details skipped over in one proof were often highlighted or better explained in its corresponding description. For someone whose training was not theoretical computer science, SSS left me with a better understanding most of the time.
- basjacobs 8y agoGreat, I'll start with SSS. Any tips for a good optimization book to read afterwards?