Experts recommend Machine Learning books
mentorcruise.com
mentorcruise.com
For people w/ a physics background, I would still recommend https://www.inference.org.uk/itprnn/book.pdf. Some of it is a bit obsolete, but then DL made a lot of stuff around generalization/overfitting somehow obsolete. It makes a lot of connection between different kind of approaches in ML, information theory, (Bayesian) statistics, and physics.
It is not a very good book if you only care about applications (in which case the Keras book, for beginner, or fastai/etc. are much more appropriate).
My guess is that information theory went through its hype phase and much of the ideas are so pervasive across real systems that people forget how important those connections are.
To tell you its importance, skim this work on information-theoretic probing [1]. I find this so satisfying. Its most famous alternative, linear probing, always felt inelegant. This paper experimentally shows how terribly linear probing fails.
Speech and Language Processing From Dan Jurafsky,
Available at: https://web.stanford.edu/~jurafsky/slp3/
Neuro-linguistic programming definitely had its day. It seems to have fallen out of fashion in the last 3 decades (it was really just a phenomenon in the 80s -- I remember those days) so it's quite likely that modern references to NLP don't refer to it.
I usually read NLP as "Nonlinear Programming" (nonlinear optimization) which is the community I come from. This acronym is not widely used outside the community so if I'm not reading the optimization literature, I'm pretty sure NLP doesn't refer to it.
Natural language processing is more in vogue these days, so that tends to be my default reading. The term itself seems to have existed for decades, so it's not like it came after the others but this is the most likely reading today.
There are some nonzero number of papers referring to NLP (as in nonlinear programming) in ML, mostly for the purposes of constrained optimization, but I agree with your current breakdown.
So is studying all that stuff just for being a MLE/ data engineer worth it, if you are already a software developer (full stack)?
I think this varies pretty widely based on employer, e.g. if you are at a smaller company (and you show interest and have the necessary skills) then you're much more likely to be able to contribute on the modeling side. It's easier to get there if you have an advanced degree, but definitely not necessary.
That being said, IMHO book lists like this aren't very useful because there's no incentive to keep them short and realistic. Reading seminal papers and implementing them is a different learning philosophy, maybe, but lists like this are probably more feasible to complete: https://dennybritz.com/blog/deep-learning-most-important-ide...
however, it remains intrinsically very interesting and can be fun to learn about. more so than most resume items.
[0] https://marxistleninist.wordpress.com/2005/01/20/study-guide...
I wish HN had a parallel community for these kind of silly posts. I use it as a worksafe site to take a break at work and sometimes jokes like this, while not being in the spirit of HN, are things I very much appreciate.
Satire and parody are probably the hardest to pull off. They’re often at someone’s expense, and that is frowned upon; even when they’re not intentionally so, someone with different points of reference may read it as such.