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In machine learning, hands down these are some of the best related textbooks:

- [0] Pattern Recognition and Machine Learning (Information Science and Statistics)

and also:

- [1] The Elements of Statistical Learning

- [2] Reinforcement Learning: An Introduction by Barto and Sutton

- [3] The Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio

- [4] Neural Network Methods for Natural Language Processing (Synthesis Lectures on Human Language Technologies) by Yoav Goldberg

Then some math tid-bits:

[5] Introduction to Linear Algebra by Strang

----------- links:

- [0] [PDF](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%...)

- [0][AMZ](https://www.amazon.com/Pattern-Recognition-Learning-Informat...)

- [2] [amz](https://www.amazon.com/Reinforcement-Learning-Introduction-A...)

- [2] [site](https://www.deeplearningbook.org/)

- [3] [amz](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)

- [3] [pdf](http://incompleteideas.net/book/bookdraft2017nov5.pdf)

- [4] [amz](https://www.amazon.com/Language-Processing-Synthesis-Lecture...)

- [5] [amz](https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)

I have to disagree with The Deep Learning book. I don't find it a good book for anyone. For beginners it's too advanced/theoretical and for experienced ML scientists it's entirely too basic. I very much agree with this review on Amazon [1].

For the former, I would recommend Hands-On Learning with Scikit-Learn and Tensorflow

[1] https://www.amazon.com/gp/customer-reviews/R1XNPL1BX5IVOM/re...

>For beginners it's too advanced/theoretical and for experienced ML scientists it's entirely too basic.

As a scientist coming to deep learning from another field, I found Courville et al to be pitched at the perfect level.

+1 for Elements. I started with Introduction to Statistical Learning and then graduated to Elements as I learned more and grew more confident. Those are fantastic books.

Could you elaborate how you switched to Elements? I am curious if it makes sense for one to go through both books in sequence.

As an engineer who hadn't studied that type of math in quite a while, Elements was pretty tough and I was getting stuck a lot.

ISLR introduces you to many of the same topics in a less rigorous way. Once I was familiar with the topics and had worked through the exercises, Elements became much easier to learn from.

If you reading Elements is difficult then I would recommend Introduction.

I'm not sure if reading Introduction will prepare you for Elements so much as it will just give you some knowledge you can use and see if it makes sense for you and what you want to do to go and (re)learn some of the math tidbits that you need for Elements.

For regression I really like Frank Harrell's Regression Modeling Strategies. http://biostat.mc.vanderbilt.edu/wiki/Main/RmS

Frank Harrell writes a lot of great stuff and his answers on the Cross Validated Stack Exchange site are worth just reading even if you didn't think you wanted to ask the question they reply to.

His blog, http://www.fharrell.com, also contains interesting posts.

I recently read Seber and Lee, Linear Regression Analysis, and highly recommend it.


>[5] Introduction to Linear Algebra by Strang

People seem to love this textbook - and understandably so because it's very approachable. But I really struggled with how informal the tone was, and how friendly it was. Perhaps I'd grown too accustomed to the typical theorem -> proof -> example -> problem set format.

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