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This book looks impressive. There's a chapter on the unreasonable effectiveness of Deep Learning which I love. Any other books I should be on the lookout for?


This presentation from Deep Mind outlines some foundational ML books: https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFp...

For the impatient, look into slide #123. Essentially, the recommendations are Murphy, Gelman, Barber, and Deisenroth.

Note these slides have a Bayesian bias. In spite of that, Murphy is a great DL book. Besides, going through GLMs is a great way to get into DL.


Reality has a well known Bayesian bias…

Joking aside, these slides are excellent! Is there an associated video or course that they were a part of?


No, these were part of a conference held at Tubingen in 2020.


What is a "Bayesian bias"?


I meant the presenter is discussing ML from a Bayesian point of view, which is interesting, but not something you need if your aim is just to understand deep learning.


Yes, it looks very impressive indeed and it has the potential to be the seminal textbook on the subject.

Fun facts, the infamous Attention paper is closing in to reach the 10K citations, and it should reach this milestone by the end of this year. It's probably the fastest paper ever to reach this significant milestone. Any deep learning book written before the Attention paper should be considered out of date, and needs updating. The situation is not unlike an outdated Physics textbook with Newton's laws but devoid of the infamous Einstein's equation of energy equivalence.



I wish it wasn't an X post. Can't see responses at all without an account.


Use nitter to go around X authwalls: https://nitter.net/suhail/status/1728676402864812466




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