numerical-linear-algebra VS pml-book

If you really like to dig into math, I liked the Udacity course on Intro to Deeplearning with Pytorch. Also, the Stanford course CS231n Convolutional Neural Networks for Visual Recognition is a good place to understand some basics. Other two courses to get you jumpstarted are Practical Deep Learning for Coders and Linear Algebra Course by FastAI

I've also bookmarked Fast.Ai Computational Linear Algebra for Coders. https://github.com/fastai/numerical-linear-algebra/blob/master/README.md

In that case, I would probably be temped to teach a numerical methods of linear algebra course using NumPy / Numba. Something like https://github.com/fastai/numerical-linear-algebra or https://pythonnumericalmethods.berkeley.edu/notebooks/Index.html

Computation Linear Algebra Lectures Study Material To do after completing curricula.

Una cunoscuta pe 'reinforced learning': https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf Doua foarte recente: https://github.com/probml/pml-book/releases/tag/2022-08-08 https://github.com/probml/pml2-book/releases/tag/2022-12-24

Kevin Murphy - Probabilistic Machine Learning

I don't know about this book, but there are August 2022 PDFs of "Probabilistic Machine Learning : An Introduction" and "Probabilistic Machine Learning : Advanced Topics" by Kevin Murphy available here: https://github.com/probml/pml-book

Is that recent enough?

Check these out: https://github.com/probml/pml-book

Diff from 2012 book, directly from the books website

"This version is being sent to MIT Press, and will appear in hard copy late 2021/ early 2022."

https://github.com/probml/pml-book/releases/tag/camera

If you want to have better interactions with the readers, then you can consider creating a GitHub repository for the book (e.g., https://github.com/probml/pml-book and https://github.com/mml-book/mml-book.github.io).