Deep Learning – Foundations and Concepts (Chris Bishop) | Hacker News Reader

Deep Learning – Foundations and Concepts (Chris Bishop)

bishopbook.com

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Two other free books, just published.

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

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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...

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Dive into Deep Learning

https://d2l.ai/

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

I have looked through Prince's book (the free PDF) and think it is reasonably modern and pretty good. I'll probably pick up a copy at some point.

I have the new d2l book. At ~25 USD it is hard not to recommend it... the paperback print (color!) quality is good. I prefer reading print books so, for me, it is a good companion to the website, which is awesome.

Thank you very much, expecially for the comment about the d2l book. An amazon review said that it was a low quality print.

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

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.

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.

Same price from publisher: https://link.springer.com/book/10.1007/978-3-031-45468-4

Price fixing at Amazon with books also included publishers: https://www.nytimes.com/2022/01/26/technology/amazon-price-f...

It is a new book, so my question is, is this title worth 90 bucks? Nope probably not. Maybe 9.99 for an ebook would be reasonable.

To paraphrase: Do you want piracy, because this is how you get piracy...

I'll always support efforts at making education more accessible, but 90$ is pretty cheap for a textbook of this size. And it's a hardcover!

And it's available to read for free online, in its entirety, on the author's website.

It's hard to beat this proposition.

> It is a new book, so my question is, is this title worth 90 bucks?

IMO, yes. I'll be ordering a copy. But "different strokes for different folks" and all that.

The ebook seems free from the first link you mention (https://link.springer.com/book/10.1007/978-3-031-45468-4)

I'm seeing a price of $69.99 for the ebook. Maybe you're accessing that site through your University and they have a subscription or something?

Springer does sales all the time. I got the 3rd edition of Peter Corke's robotics book for 17 USD directly from springer earlier this year.

I also got Nathan Ida's book on Electromagnetic, hardcover, for ~40 USD direct from springer.

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.

PRML == Pattern Recognition and Machine Learning

see https://www.microsoft.com/en-us/research/publication/pattern... for the free PDF copy.

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?

Jurafsky's 3rd edition draft of his NLP book and Simon Prince's DL book both have chapters on transformers, and the latter also on diffusion. Both have official free pdf versions.

https://web.stanford.edu/~jurafsky/slp3/

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

> 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?

Looks like the embedded slideshow is what is "free". They want you to purchase the actual book.

Instructions are misleading. If you click on the Issuu see full screen icon, you can read the whole book online.

https://issuu.com/cmb321/docs/deep_learning_ebook

It is free for me from here: https://link.springer.com/book/10.1007/978-3-031-45468-4

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/

Murphy's book is about probabilistic/Bayesian machine learning whereas this, according to the preface is 'almost entirely non-Bayesian'. Murphy's book is also a lot more general, this one is about the deep learning subset of ML.

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.

You try karpathys course?

Didn't know about this, thank you.

For reference, this is the course: https://karpathy.ai/zero-to-hero.html

I'm working through the courses here: https://phaseai.com/resources/free-resources-ai-ml-2024

I'm avoiding Azure or Google courses for the same reason. FastAI references its own (free) library but you can also do without it if you really try.

Both Karpathy and fast.ai are good resources. Karpathy is "bottom up" (start from first principles and build on it) while fast.ai is "top down" (you start w/ working examples and gradually "peel off" to understand it).

Both approaches have merit, and it may come down to personal preferences.

  - karpathy: https://karpathy.ai/zero-to-hero.html
  - fast.ai: https://course.fast.ai/Lessons/lesson1.html

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

Series: https://www.statlearning.com

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..

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

https://www.microsoft.com/en-us/research/publication/pattern...

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.

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.

After ISL.

Awesome!

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