There is a Japanese word, tsundoku (積ん読), which means buying and keeping a growing collection of books, even though you don’t really read them all....
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Great choices and these are in my virtual bookshelf.
O’Reilly: Data Science from Scratch with Python (I used for my UCLA Extension: Intro. To Data Science). Note the book's GitHUb repository has code in Python 3 too. Enjoy re-reading it.
Springer: Introduction to Statistical Learning (I used for my UCLA Extension: ML in R ).
Great dataset and well written on statistics information that helped me with revision/refresher.
Deep Learning by Goodfellow, Bengio et al. ( Recommended as an optional text for my current UCSD Extension: Deep Learning With TensorFlow & Keras ).
Thanks for posting. :-)
I had read O’Reilly: Data Science from Scratch with Python but it does not satisfy my need to find out what is regression, softmax, dense, one hot, or any other methodologies. Do you have any book reference for that?
Thanks for asking!
It's true, Data Science from Scratch is a lot broader, and doesn't specialize in Machine Learning concepts -like the ones you bring up- only.
Most of the things you mentioned are usually associated with Deep Learning, and you'd get a very in-depth explanation of all of them, plus an intuition of when to use them, from Goodfellow's Deep Learning book.
Thanks for the recommendation, I have been waiting for a copy of
Data Science from Scratch with Pythonfor a while now from Amazon.I've wanted to read The Hundred-Page Machine Learning Book by Andriy Burkov. I haven't had a chance, but I have only heard good things about it.
I'd never heard of this book before, I'll look into it later!
Thank You. Any suggestions for a mediocre math person with hands on Ruby knowledge?
Well, my first suggestion would be don't call yourself mediocre!
I'm sure you can learn any of these things if you set your mind to it and work on it for enough time. It may take longer, or shorter, but you'll eventually get there.
On the practical side, I'll say read these books, but also code a lot, and keep everything on GitHub. That's your portfolio.
If you keep grinding, you'll realize one day that you've levelled up!
Thank You again!
Do you have any recommendations for books on revising math/stats as a prerequisite to diving into these?
Well, Bengio's Deep Learning actually has a very big Statistics chapter which covers everything you should need, and Data Science from Scratch dedicates a big part of its first chapters to introducing you to Statistics, assuming basically no prior knowledge.
So either of those, depending on how confident you feel, should be good to start!
You have good taste. Thanks for sharing.