Found 7 comments on HN
formalsystem · 2018-10-14 · Original thread
Feel free to PM if you need more specific recommendations since it was really hard to try and come up with a concise list. I've seen a lot of friends and colleagues struggle with some specific popular books. Sometimes its OK not to like the way someone writes even if they're really smart and super famous.

My two favorite applied ones are

* Data science from scratch because it's one of the most concise and logical expositions of most ML algorithms in simple python that you'll remember and be able to reproduce from scratch if need be https://www.amazon.com/Data-Science-Scratch-Principles-Pytho...

* Deep learning with python as a next step since it covers more complicated neural net architectures using Keras so not from scratch but with clear code that you'll again remember

* Designing data-intensive apps because it'll prepare you for most challenges you'll face as a data engineer

On the theoretical side

* All of statistics: it's been recommended here on hacker news many times and for good reason. Its scope is very ambitious and it avoids the trap that math books fall into of leaving too many seriously hard steps an exercise to the reader. https://www.amazon.com/All-Statistics-Statistical-Inference-...

* Convex optimization which will give you the theoretical foundation to understand mathematically supervised and unsupervised learning http://web.stanford.edu/~boyd/cvxbook/

I'll also add a reference to a Reinforcement Learning resource because I'm trying to build a game AI company using its ideas. Simple RL with TF because being able to program virtual robots to do stuff is really cool and this is one of the easier ways to better grasp RL https://medium.com/emergent-future/simple-reinforcement-lear.... Probably worth studying in conjunction with RL an introduction which is more theoretical and has a Q&A like approach to understanding the material which was interesting.

lukego · 2017-07-22 · Original thread
What a beautiful presentation!

Tangentially: I am really enjoying the book "All of Statistics" as a reference for better understanding things like histograms, kernel density functions, etc, and their parameters.

https://www.amazon.com/All-Statistics-Statistical-Inference-...

Zuider · 2016-05-15 · Original thread
Sadly, no link to free eBook, which is not surprising because it seems that the book is still in print, having been released as recently as 2004, and updated in 2005 and 2013.

This post links to the website supporting the book and provides links to errata, code and data. The links on the page to Springer and Amazon are broken: Here are valid links:

http://www.springer.com/de/book/9780387402727

http://www.amazon.com/All-Statistics-Statistical-Inference-S...

Here is the Google Books link:

https://books.google.ie/books?id=th3fbFI1DaMC&printsec=front...

pm90 · 2012-05-28 · Original thread
A few days ago I found this gem of a book that I think many here would find interesting (if you don't already know it) : http://www.amazon.com/All-Statistics-Statistical-Inference-S...
hc · 2009-08-23 · Original thread
here is a course i have been working through that describes some learning algorithms: http://courses.csail.mit.edu/6.867/lectures.html i also like this book: http://www.amazon.com/All-Statistics-Statistical-Inference-S...

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