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However, it's pretty obvious that they are related since CCA is (or should be) well-known to be among the original unsupervised learning algorithms. It's the progenitor of the field. It works, it always did. Just like logistic regression for classification. Deep learning is about putting in huge computational effort for the extra few percent.
This is like saying that Gauss deserves the credit for LLMs because he came up with least-squares regression, which was the progenitor of supervised learning. Yes, there is a chain of discoveries leading back, but when you give the credit that far back, it's just insulting to the hard work that came inbetween.
Gauss and Hotelling are famous enough as it is.
(Before anyone asks, I'm not shilling for JEPA, I just think this argument is reductive for all of unsupervised and semi-supervised learning.)
The latter is covered well by Andrej Karpathy's videos and by just playing around with current models and other tutorials in a small test environment. You don't need to know very much, there's a lot of low-hanging fruit.
For the former, the field is moving rapidly and most of the innovations are coming from papers. Any book that claims to cover deep learning is almost inevitably outdated. Find a university or institution near you and see if they have an undergraduate reading group on deep learning that is open to the public to attend. Mine does, and it's really helpful for staying up to date with the latest ideas. "Probabilistic Machine Learning" by Murphy contains the material that I would consider prerequisite if you want to understand the ideas which underpin modern deep learning (even if it contains virtually no deep learning in it), and I would hope that any student or colleague of mine would be familiar with most of it. But I'm not sure it's good to learn from, and picking all that up takes a while to be honest.
This is confusing. Are you referring to the old 2012 version?
Volumes 1 & 2 (2022-3) contain a substantial amount of deep learning [1], including relatively recent developments.
There's also a new RL volume getting written, with some drafts deposited in arXiv [2].
[1] https://probml.github.io/pml-book
[2] https://arxiv.org/pdf/2412.05265