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Understanding Deep Learning

udlbook.github.io

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Re: Understanding Deep Learning

#51

Post by bjacobt »

wilkystyle wrote: post #49
GeneralMayhem wrote: post #41

Earlier quoted context omitted.

The most important thing to learn for most practical purposes is what the thing can actually do. There's a lot of fuzzy thinking around ML - "throw AI at it and it'll magically get better!" Sources like Karpathy's recent video on what LLMs actually do are good anti-hype for the lay audience, but getting good practical working knowledge that's a level deeper is tough without working through it. You don't have to memor…

> Sources like Karpathy's recent video on what LLMs actually do are good anti-hype for the lay audience Which video is this?

I believe op means Intro to Large Language Model

https://youtu.be/zjkBMFhNj_g?si=XQQ3p92ajuQYOyqN

Re: Understanding Deep Learning

#52

Post by tysam_and »

ldjkfkdsjnv wrote: post #4

I spent a decade working on various machine learning platforms at well known tech companies. Everything I ever worked on became obsolete pretty fast. From the ML algorithm to the compute platform, all of it was very transitory. That coupled with the fact that a few elite companies are responsible for all ML innovation, its oxymoronic to me to even learn a lot of this material.

Highly, highly disagree.

If it became obsolete, then y'all were doing the new shiny.

The fundamentals don't really change. There are several different streams in the field, and there are many, many algorithms with good staying power in use. Of course, you can upgrade some if you like, but chase the white rabbit forever, and all you'll get is a handful of fluff.

Re: Understanding Deep Learning

#53

Post by mitthrowaway2 »

nextos wrote: post #34
msie wrote: post #3

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.

What is a "Bayesian bias"?

Re: Understanding Deep Learning

#54

Post by giardini »

quchen wrote: post #27
mnky9800n wrote: post #19

Earlier quoted context omitted.

Which drama of last week are you referring to? The one about the openai guy saying it's all just the data set? Or something else?

Their CEO was fired, hired by Microsoft, took a bunch of people with him, and is now back at the company

Did the "bunch of people" also return (from Microsoft to OpenAI)?

Re: Understanding Deep Learning

#55

Post by lngnmn2 »

nsxwolf wrote: post #9

As someone who missed the boat on this, is learning about this just for historical purposes now, or is there still relevance to future employment? I just imagine the OpenAI eats everyone's lunch in regards to anything AI related, am I way off base?

[dead]

Re: Understanding Deep Learning

#56

Post by quickthrower2 »

nsxwolf wrote: post #9

As someone who missed the boat on this, is learning about this just for historical purposes now, or is there still relevance to future employment? I just imagine the OpenAI eats everyone's lunch in regards to anything AI related, am I way off base?

This would be like learning how your CPU / Memory works, even though JS is eating everyone's (web front-end) lunch.

So yes if you are prompt engineering, and wondering why X works and sometimes it doesn't, and why any of this works at all, it is good to study a bit.

Re: Understanding Deep Learning

#57

Post by teleforce »

msie wrote: post #3

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?

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.

Re: Understanding Deep Learning

#58

Post by nextos »

mitthrowaway2 wrote: post #53
nextos wrote: post #34

Earlier quoted context omitted.

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.

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.

Re: Understanding Deep Learning

#59

Post by contrarian1234 »

It's very hard to judge a book like this... (based on a table of contents?)

Who is the author ?

Have they published anything else highly rated ?

Are there good reviews from people that know what they're talking about?

Are there good reviews from students that don't know anything ?

Re: Understanding Deep Learning

#60

Post by arman_hkh »

contrarian1234 wrote: post #59

It's very hard to judge a book like this... (based on a table of contents?) Who is the author ? Have they published anything else highly rated ? Are there good reviews from people that know what they're talking about? Are there good reviews from students that don't know anything ?

Marcus Hutter on his [Marcus' AI Recommendation Page]: "Prince (2023) is the (only excellent) textbook on deep learning."
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