51–60 of 114 posts
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 At this point it is pretty much outdated. Actually, anything specific to NNs is already outdated by release.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…
- 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 would give as required reading for anyone interested in understanding some deep learning fundamentals.
- Deep Learning - goodfellow and bengio - seems like it would be hard to get through without a reading group not exactly a APUE or K&R type reading experience but I haven't spent enough time with it.
If you haven't taken a Linear Algebra or Differential Equations class its useful stuff to know for ML/DL theory but not fully necessary to do applied work with modern high level libraries, but definitely having a strong understanding of basic matrix math is useful.
If you have interests in natural language processing theres a couple good books:
- Natural Language Processing with Python - Bird Klein, Loper, is a great intro to NLP concepts and working with NLTK which may be a bit dated to some but I would definitely recommend, and its online for free. Great examples.(https://www.nltk.org/book/)
- Speech and Language Processing - Dan Jurafsky and James H. Martin - is good, though I have only spent much time with the pre-print
And then theres a lot of papers that are good reads. Let me know if you have any questions or want a list of good papers.
If you just want to get off the ground and start playing with stuff and building things I'd recommend fast.ai's free online course - its pretty high level and a lot is abstracted away but its a great start and can enable you to build lots of cool things pretty rapidly. Andrew Ng's online course also is quite requitable and will probably give you a bit more background and fundamentals.
If I were to choose one book from the bunch it would be Chollet it gives you pretty much all the building blocks you need to be able to read some papers and try to implement things yourself and I find building things a much more satisfying way to learn than sitting down and writing proofs or just taking notes but thats just my preference.
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.)
If you just want to apply well known things to well known things, sure you’re right. But as soon as things go wrong, I couldn’t imagine how much more inefficient my iteration cycles would be trying to do novel work without understanding linear algebra (for some kinds of novel work) or calc (for other kinds of novel work). I think you kinda get at this when you say it’s not necessary but it helps. It’s not necessary, but it helps a lot with anything off the beaten track.
You may want to also consider this one: Artificial Intelligence, a modern approach – Stuart Russell, Peter Norvig
It’s probably a bit off the beaten path, but I can highly recommend Probability Theory, The Logic of Science, by E. T. Jaynes. In the opening chapter Jaynes describes a hypothetical system he calls “The Robot”. He then lays out the mathematics of the “The Robot’s” thinking in detail: essentially Bayesian probability theory. This is the best summary of an ideal ML/AI system I’ve come across. It’s also very philosophic…
I'm so sad the editor chose not to publish Jaynes' C snippets because "they were too cryptic." They would've helped clarify the ideas greatly. It's a good book, but I don't know how it's related to ML. My own answer would be "Just do it." Find an ML project you like and start tinkering around. But everyone learns differently, so maybe there's a book that can replace experience.
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…
Was that supposed to be An Introduction to Statistical Learning[1] or maybe Introduction to Statistical Relational Learning[2]? I don't think there is a book titled Introduction to Elements of Statistical Learning?
Earlier quoted context omitted.
(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 disagree strongly. In your analogy, if the compiler broke down all the time, you would probably need to understand assembly to do programming. ML is amazing today, but still kinda sucks. In general you’ll have a bunch of failures on the way to a successful novel application, so it’s more critical to understand what’s going on under the hood in ML than in your programming analogy. If you just want to apply well know…
And certainly, if you're one of those people who can pull it off, studying ML from first principles is probably an advantage. I just wince every time since I wouldn't have gotten into ML in the first place if I had to start with a big Calculus tome. There are probably a lot of people like me out there.
→ Harrison Kinsley, Daniel Kukiela, Neural Networks from Scratch, https://nnfs.io, https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0Qu...
Somewhat foundational, if not in actuality, then in the intention to actually build a theory as in theory of gravitation, although not necessarily an introductory text:
→ Daniel A. Roberts, Sho Yaida, The Principles of Deep Learning Theory, https://arxiv.org/abs/2106.10165
Earlier quoted context omitted.
Many of the suggestions so far are assuming you have taken undergraduate linear algebra and calculus. I'd start with those two subjects, you really can't build a foundational understanding of modern AI techniques without them.
i did linear algebra and calculus using strang and spivak textbooks. Those were classes i enjoy the most. But most of that stuff has atrophied from my brain over the years, do you recommend redoing those courses fast or can i learn when i need it on demand basis.
"Introduction to Statistical Learning" - https://www.statlearning.com/ (there's also "Elements of Statistical Learning" which is a more advanced version) AI: A Modern Approach - https://aima.cs.berkeley.edu/
The explanation, examples, projects, math- all are crisp.
As the name suggests, it is only an introduction (unlike CLRS). And it does serve as a great beginners' book giving you proper foundation for the things that you learn and apply in the future.
One thing people complain about is it being written in R, but no serious hacker should fear R, as it can be picked up in 30 minutes, and you can implement the ideas in Python.
As someone with industry experience in Deep Learning, I will recommend this book.
The ML course by Andrew Ng has no parallel, though. One must try and do that course. Not sure about the current iteration, but the classic one (w/ Octabe/MATLAB) was really great.