
Experts recommend Machine Learning books - dmonn
https://mentorcruise.com/books/ml/
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henrik_w
For a survey of AI and ML I really liked "Artificial Intelligence – A Guide
for Thinking Humans" by Melanie Mitchell. I've written a summary of it here:
[https://henrikwarne.com/2020/05/19/artificial-
intelligence-a...](https://henrikwarne.com/2020/05/19/artificial-intelligence-
a-guide-for-thinking-humans/)

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cdavid
The list is decent, but not exactly original.

For people w/ a physics background, I would still recommend
[https://www.inference.org.uk/itprnn/book.pdf](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).

~~~
activatedgeek
David MacKay was an absolute rockstar and this book is grossly underrated
among beginners in machine learning. This should be THE complementary
reference for anyone who uses the Bishop book. Both these books follow a
philosophy which some people may not completely agree with, that from the
"church of Bayes".

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.

[1] [https://arxiv.org/abs/2003.12298](https://arxiv.org/abs/2003.12298)

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melenaboija
For NLP I would maybe add

Speech and Language Processing From Dan Jurafsky,

Available at:
[https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/)

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inopinatus
Slightly crestfallen that the “ML” here is machine learning and not the
programming language. I still refer to my vintage paperback of L. C. Paulson’s
_ML for the Working Programmer_ from time to time.

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ru552
I still read NLP as Neuro-linguistic programming. Every. Time.

~~~
wenc
So I have a little disambiguation heuristic:

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.

~~~
LolWolf
Oh, for a second I thought the GP _was_ referring to nonlinear programming.
Then I remembered this is an ML thread.

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.

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newtohn99
As a new grad (bachelors swe), is it worth it to jump on the ML hype train ? I
see modelling is almost always only open to phds/masters.

So is studying all that stuff just for being a MLE/ data engineer worth it, if
you are already a software developer (full stack)?

~~~
voidray
> I see modelling is almost always only open to phds/masters.

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...](https://dennybritz.com/blog/deep-learning-most-important-ideas/)

~~~
dougmany
This is a great approach to learning. It explains how the field got to where
it is and allows the reader to go as deep as they want.

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horsemessiah
State and Revolution is where I'd recommend people start for ML ;)

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CinchWrench
Slightly Meta:

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.

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hndude
I believe its called reddit

~~~
mhh__
The only difference I find between reddits and HN are the amount of memes and
the delusions of grandeur.

