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Deep Learning – Foundations and Concepts (Chris Bishop)

by armcat on 12/11/23, 9:01 PM with 36 comments

• by _giorgio_ on 12/12/23, 11:22 AM

  Two other free books, just published.

  I'm interested in your opinion about them; both have pytorch code (notebooks).

  ___________________________

  Understanding Deep Learning

  by Simon J.D. Prince

  Published by MIT Press Dec 5th 2023.

  https://udlbook.github.io/udlbook/

  https://www.amazon.com/Understanding-Deep-Learning-Simon-Pri...

  ___________________________

  Dive into Deep Learning

  https://d2l.ai/

  https://www.amazon.com/Dive-into-Learning-Aston-Zhang/dp/100...

• by maroonblazer on 12/12/23, 2:14 AM

  And if you need to brush up on your math, "Mathematics for Machine Learning" is available for free, by the authors.

  https://mml-book.github.io/book/mml-book.pdf

• by arolihas on 12/11/23, 10:01 PM

  The same Bishop who wrote PRML? Nice! Looking through it does seem to be very up to date, although I wish there was a little bit more on topics like geometric deep learning and flow matching. Of course if every niche had a more thorough treatment this book would be impossibly long but more than a couple paragraphs would have been cool.

• by breadwinner on 12/11/23, 11:34 PM

  Here it is on Amazon: https://www.amazon.com/Deep-Learning-Foundations-Christopher...

  Now that Amazon has competition they are not offering even $1 of discount. In the old days you'd get at least $20 off list price.

• by hiddencost on 12/12/23, 1:19 AM

  One of the few people who could pull the three horseman of AI to provide book blurbs.

  I'm about 10 feet from PRML right now, more than a decade after I got it.

• by gigafuture on 12/12/23, 3:30 AM

  I skimmed through the book and it looks great! I'm about to buy it!

  Besides this book are there any in the same league that are applicable to learn more about the diffusion and transformer model architectures?

• by fargoth on 12/11/23, 11:14 PM

  > A free-to-use eBook version is available by clicking on the book icon below.

  Does anyone see a book icon? Or are we meant to flip through a slideshow embedded in the website?

• by laichzeit0 on 12/12/23, 4:02 AM

  I’d be interested to know how this stacks up against Kevin Murphy’s recently released two volume Probabilistic Machine Learning books. [1]

  [1] https://probml.github.io/pml-book/

• by ornornor on 12/12/23, 6:51 AM

  Can anyone recommend an online course for practically “learning AI”?

  I’ve tried the hugging face course but got discouraged at the not quite working examples and colab books. There is also the Amazon and MS courses but I’d rather learn in a neutral way rather than a vendor-centric way.

• by tsinik on 12/15/23, 12:31 PM

  An introduction to Statistical Learning with Applications in Python https://hastie.su.domains/ISLP/ISLP_website.pdf.download.htm...

  Series: https://www.statlearning.com

• by Maro on 12/14/23, 12:39 PM

  I looked at the ToC and clicked into some of the chapters. It seems like this is more like a "Fundamentals of Machine Learning" book, as a lot of it is not really specifically about Deep Learning. Chapters that specific to Deep Learning seem to be 6-10, 12, 13 and 17-20. Nothing wrong with the contents, but I think the title is a bit misleading..

• by tasubotadas on 12/11/23, 11:20 PM

  Big fan of his PRML book. Can't wait to get my hands on this.

• by lysecret on 12/12/23, 7:25 AM

  Uhhh that is actually looking fantastic. Especially for slightly advanced beginners to the space.

  It has now been 8 years since I went through the Elements and Bishop Books in Uni. Now I want to read this over the Christmas break.

• by nocoder on 12/12/23, 3:20 AM

  where does this fit in? I am reading Intro to statistical learning, so wondering if this should come after it or I can directly jump to this when I reach the unsupervised ML techniques.

• by wywwzjj on 12/12/23, 5:13 AM

  Awesome!