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There is another approach[0] centered on a few key textbooks. The goal is to prepare you for fully grokking Deep Learning[1] and Elements of Statistical Learnin
by ipnon 5y ago
There is another approach[0] centered on a few key textbooks. The goal is to prepare you for fully grokking Deep Learning[1] and Elements of Statistical Learning.[2]
[0] https://www.dropbox.com/s/mffzmuo9fvs5j6m/Study_Guide.pdf https://www.dropbox.com/s/mffzmuo9fvs5j6m/Study_Guide.pdf
[1] https://www.deeplearningbook.org/ https://www.deeplearningbook.org/
[2] https://web.stanford.edu/~hastie/ElemStatLearn/ https://web.stanford.edu/~hastie/ElemStatLearn/
- tubby12345 5y agoGoodfellow's is not good. Hodgepodge of material with scattered theorems and way too much attention paid to GANs (surprise surprise). ESL is very good but at this point it's the TAOCP of ML book recommendations (besides have steep prereqs). This is a way more approachable and yet still through book on DL.
- nmfisher 5y agoDisagree on Goodfellow, personally I found it the most approachable introduction to neural networks & backprop. To be fair, I think this must have been the pre-GAN edition so maybe it's changed a bit since then. ESL is a fantastic reference but I wouldn't recommend it as a textbook, it's just too terse.
- disgruntledphd2 5y ago> ESL is a fantastic reference but I wouldn't recommend it as a textbook, it's just too terse. I dunno, it's really well written (at least chapters 1-9 are, because they were re-written) and the graphs are to die for. It definitely has a bunch of theorems and proofs that may not be necessary, but the overall flow isn't lost by skipping the ones above your mathematical maturity level. To be fair though, I also love TAOCP, and have worked through 1.5 chapters at this point.