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Ask HN: What are the foundational texts for learning about AI/ML/NN?

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Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#81

Post by jtmcmc »

TaupeRanger wrote: post #37

There are none anymore. We now know that throwing a bunch of bits into the linear algebra meat grinder gets you endless high quality art and decent linguistic functionality. The architecture of these systems takes maybe a week to deeply understand, or maybe a month for a beginner. That's really it. Everything else is obsolete or no longer applicable unless you're interested in theoretical research on alternatives to…

This is definitely a take that ignores the massive amount of utility for ML that exists outside of generative images and NLP on the one hand and on the other vastly misrepresents the time it takes to understand a model, assuming one does not already have a background in CS, linear algebra and in particular matrix calculus, probability, stats, etc...

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#82

Post by jtmcmc »

digitalsushi wrote: post #74

Are there obvious paths into these spaces for someone stuck over in devops/infrastructure/platform engineering? Or is it too far a hop to really find a direct path in? Let me ask a slightly different way - can someone like me get into a job like these, without needing some more college? My day job is wrapping up OS templates for people with ML software and I always wonder what they get to go do with them once they tu…

if you're already doing a job at a company that does this stuff, can you talk to people about wanting to change teams and learn?

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#83

Post by jeffreyrogers »

cyber_kinetist wrote: post #48
TaupeRanger wrote: post #37

There are none anymore. We now know that throwing a bunch of bits into the linear algebra meat grinder gets you endless high quality art and decent linguistic functionality. The architecture of these systems takes maybe a week to deeply understand, or maybe a month for a beginner. That's really it. Everything else is obsolete or no longer applicable unless you're interested in theoretical research on alternatives to…

You still need to understand some basic theory/math about probabilistic inference (along with some knowledge of linear algebra), or else you’ll get a bit overwhelmed by some of the equations and not understand what the papers are talking about. PRML by Bishop is probably more than enough to start reading ML papers comfortably though. (This would probably be too easy for a competent math major, but not all of us are t…

I'm not sure why you're getting downvoted. I find it hard to believe that someone without a decently strong math background could make sense of a modern paper on deep learning. I have a math minor from a good school and had to brush up on some topics before papers started making sense to me.

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#84

Post by jeffreyrogers »

jimbokun wrote: post #66
rg111 wrote: post #62

Earlier quoted context omitted.

You are plain exaggerating. You can't do all of them in a few weeks. Algorithms: Lin Reg -> Log Reg -> NN -> CNN + RNN -> GANs + Transformers -> ViT -> Multimodal AI + LLMs + Diffusion + Auto Encoders SVM, PCA, kNN, k-means clustering, etc. LightGBM, XGboost, Catboost, etc. Optimization and optimizers. Application-wise: Classification, Semantic Segmentation, Pose Estimation, Text Generation, Summarization, NER, Image…

> SVM, PCA, kNN, k-means clustering Are these still relevant in the age of Deep Neural Networks?

It's not in your list but decision trees still outperform DNN on many tabular problems and can be trained faster.

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#85

Post by pablo24602 »

gerash wrote: post #72

I'd suggest these two by Kevin Murphy: Probabilistic Machine Learning: An Introduction https://probml.github.io/pml-book/book1.html Probabilistic Machine Learning: Advanced Topics https://probml.github.io/pml-book/book2.html

Working through these right now- definitely recommend them

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#86

Post by moneywoes »

TaupeRanger wrote: post #37

There are none anymore. We now know that throwing a bunch of bits into the linear algebra meat grinder gets you endless high quality art and decent linguistic functionality. The architecture of these systems takes maybe a week to deeply understand, or maybe a month for a beginner. That's really it. Everything else is obsolete or no longer applicable unless you're interested in theoretical research on alternatives to…

What resources are there to understand in a month?

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#87

Post by moneywoes »

bilsbie wrote: post #75

If anyone is just starting and out wanting to do a study group let me know. I’m having trouble keeping my motivation up but I really want to get up to speed on how LLM’s work and someday make a career switch.

Im down

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#88

Post by antegamisou »

sillysaurusx wrote: post #38
PartiallyTyped wrote: post #19

I recommend against DL by Goodfellow. At this point it is pretty much outdated. Actually, anything specific to NNs is already outdated by release. You'd need the following background: - Linear Algebra - Multivariate Calculus - Probability theory && Statistics Then you need a decent ML book to get the foundations of ML, you can't go wrong with either of these: - Bishop's Pattern Recognition - Murphy's Probabilistic ML…

(I can't wait until the myth that you need linear algebra and calculus to do ML finally dies. It's like saying that you need to understand assembly to do programming. It helps, but it's far from a requirement.)

> I can't wait until the myth that you need linear algebra and calculus to do ML finally dies.

This is such a dangerously absurd claim.. but then, it speaks volumes about the abysmal state the non-research heavy AI/ML field has fallen into.

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#89

Post by poulsbohemian »

I'm sitting ten feet from my copy of Artificial Intelligence, a modern approach – Stuart Russell, Peter Norvig. While I will say it has a lot of still worthwhile basic information, I really wouldn't recommend it. It's an enormous book, so physically difficult to read, but also the bulk of the content is somewhere between dated and terse. I went through school and studied AI ten years before it was written, and I'm glad I didn't use it as an undergrad textbook - would have been overwhelming.

One of the problem with AI is exactly what you noted above - there are a lot of subcategories and my gut tells me these will grow. For the real neophyte, I'd say start with something that interests you or that you need for work - you likely aren't going to digest all of this in a month and probably no single book will meet all your needs.

Re: Ask HN: What are the foundational texts for learning about AI/ML/NN?

#90

Post by avipeltz »

rg111 wrote: post #64
avipeltz wrote: post #52

- AIMA by Russel and Norvig is a classic but I would say is more of overview of the field and for most topic areas isn't quite deep enough imo. - For deep learning specifically, a more applied text that is beautifully written and chock full of examples is Francois Chollet's Deep Learning with Python (there a new second edition out with up to date examples using modern versions of Tensorflow). The first 3 chapters I w…

Norvig-Russel has many chapters spanning hundreds of pages that are way out of date and not used anywhere. And the new things he cover are covered in a better manner and better depth in other sources. I read this book like a novel. Good for a basic overview, but the RoI is very low.

agreed
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