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- Goodfellow et al. Deep learning. MIT press, 2016. https://www.deeplearningbook.org/
And there is the book about DL with Python published by Manning in 2017, but not the book about DL with PyTorch, published by Manning in 2020?
- Stevens et al. Deep Learning with PyTorch. Manning Publications, 2020. https://www.manning.com/books/deep-learning-with-pytorch
I find it weird that this (free) book was omitted: - Goodfellow et al. Deep learning. MIT press, 2016. https://www.deeplearningbook.org/ And there is the book about DL with Python published by Manning in 2017, but not the book about DL with PyTorch, published by Manning in 2020? - Stevens et al. Deep Learning with PyTorch. Manning Publications, 2020. https://www.manning.com/books/deep-learning-with-pytorch
The article notes that "Designing Data-Intensive Applications" is perhaps not a typical data science book, but it is still very useful. I agree. It is a fantastic book - one of the best technical books I have read. I wrote why, and a summary of it here: https://henrikwarne.com/2019/07/27/book-review-designing-dat...
I have plenty of textbooks in my reading list already.
I find it weird that this (free) book was omitted: - Goodfellow et al. Deep learning. MIT press, 2016. https://www.deeplearningbook.org/ And there is the book about DL with Python published by Manning in 2017, but not the book about DL with PyTorch, published by Manning in 2020? - Stevens et al. Deep Learning with PyTorch. Manning Publications, 2020. https://www.manning.com/books/deep-learning-with-pytorch
Goodfellow's book is a poor learning experience. It only makes sense if you already know the material in the book.
The "Bayesian Inference and Machine Learning" track gives a nice foundation to anything with "log loss". After that even k-means won't be an ad-hoc algorithm.
I have Deep Learning with Python (Chollet, first edition) and Hands-On Machine Learning (Géron, first edition). Both books are highly recommended. Introduction to Statistical Learning is also available for free online: http://faculty.marshall.usc.edu/gareth-james/ISL/ Although I only read a few chapters from that book, I really like it (but I would have preferred a python version of the book). Personally, if you have…
If I had to choose 1 book as the ML bible, then it would be Murphy's (contrasted against Bishop and ESL) for the following reasons:
1. It uses CS jargon. (Bishop's book while great, uses Math/physics notation/jargon which add a barrier to entry)
2. It is more up to date and comprehensive (It covers everything from probabilistic models, traditional models, to neural networks all the way up to 2015 or so, unlike say ESL which is more introductory)
3. Everything in deep learning past 2015, is better learnt through papers/video lectures than any book. (A lot of it is intuition and not truths. There is a certain authority to books that belies our lack of understanding of NNs. Opinions on popular operations such as Dropout, Batch Norm, saliency maps have changed drastically over the last few years)
4. My ML professor used it for our upper grad level ML course and I came out very satisfied. (nothing quite like personal validation). I have read ESL and found it to be better as reading for an intro to ML course. I tried reading Bishop, and didn't like it :| )
The frequency with which Kevin Murphy's ML book gets left out of these lists in almost bewildering. If I had to choose 1 book as the ML bible, then it would be Murphy's (contrasted against Bishop and ESL) for the following reasons: 1. It uses CS jargon. (Bishop's book while great, uses Math/physics notation/jargon which add a barrier to entry) 2. It is more up to date and comprehensive (It covers everything from prob…