I spend a lot of time sniffing them out.

One thing I'll do once in a while is visit a university bookstore to get a sense of what's being used in classrooms. I ended up with a lot of my law textbooks like that. In law, I've really loved:

- Canadian Administrative Law (Guy Régimbald): https://store.lexisnexis.ca/en/categories/shop-by-jurisdicti...

- Tort Law (Horsey & Rackley): https://www.amazon.ca/Tort-Law-Kirsty-Horsey-dp-019886776X/d...

- Handbook of Canadian Higher Education Law (Shanahan, Nilso & Broshko): https://www.amazon.ca/Handbook-Canadian-Higher-Education-Law...

I've been on a maths, stats and ML/AI binge in the last year, and so far my top picks are:

- Calculus (Stewart): https://www.amazon.ca/Calculus-James-Stewart/dp/1337624187/

- Linear Algebra Done Right (Sheldon Axler): https://www.amazon.ca/Linear-Algebra-Right-Undergraduate-Mat...

- An Illustrative Guide to Multivariable and Vector Calculus (Miklavcic): https://www.amazon.ca/gp/product/3030334589/

- Probability: A Graduate Course (Allan Gut): https://www.amazon.ca/gp/product/1461447070/

- Statistical Rethinking (McElreath): https://www.amazon.ca/gp/product/036713991X/

- An Introduction to Statistical Learning (James, Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl...

- Regression and Other Stories (Gelman, Hill & Vehtari): https://www.amazon.ca/gp/product/1107676517/

- Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill): https://www.amazon.ca/gp/product/052168689X/

- Statistical and Data Analysis for Financial Engineering (Ruppert & Matteson): https://www.amazon.ca/gp/product/1493926136/

- Reinforcement Learning (Sutton & Barto): https://www.amazon.ca/Reinforcement-Learning-Introduction-Ri...

- Deep Learning (Godfellow, Bengio & Courville): https://www.amazon.ca/Deep-Learning-Ian-Goodfellow/dp/026203...

- Networks (Mark Newman): https://www.amazon.ca/Networks-Mark-Newman/dp/0198805098/

I have a ton of textbooks in my wishlist that seem noteworthy and interesting, but that I haven't gotten the chance to read yet: no promises, but you might find something in there https://www.amazon.ca/hz/wishlist/ls/1FWW046Q3RWWM

My area of expertise is biochemistry, life science, immunology, cell bio... So if anyone wants good recommendations in these topics I have plenty! :)

Pretty much any book by W. Richard Stevens, but in particular Unix Network Programming, which made a cameo appearance in Wayne's World.

Intro to Statistical Learning by Hastie, Tibshirani, James and Witten: https://www.amazon.com/Introduction-Statistical-Learning-App...

Thanks for the link.

https://www.amazon.com/Elements-Statistical-Learning-Predict...

vs

https://www.amazon.com/Introduction-Statistical-Learning-App...

The former seems to assume prior knowledge and the latter seems more beginner friendly, except it uses R (in case anyone wants the distinction).

Thanks for the link.

https://www.amazon.com/Elements-Statistical-Learning-Predict...

vs

https://www.amazon.com/Introduction-Statistical-Learning-App...

The former seems to assume prior knowledge and the latter seems more beginner friendly, except it uses R (in case anyone wants the distinction).

The best introductory book and also the most cohesive one is "Learning from Data" from Yaser Abu-Mostafa, accompanied by great video lectures:

http://amlbook.com/

It differs from other books in that all the material is treated from the unified perspective of statistical learning theory and VC dimension, as a result the book feels less like a hodgepodge of unrelated techniques and more like an introduction to a coherent field.

Hastie and Tibshirani also have a new, less demanding mathematically book out:

http://www.amazon.com/Introduction-Statistical-Learning-Appl...