1. AI Engineering: Building Applications with Foundation Models by Chip Huyen.

We’ll probably read it in the study group "AI from Scratch".

2. Alice’s Adventures in a Differentiable Wonderland: A Primer on Designing Neural Networks (Volume I) by Simone Scardapane.

Looks sweet and short, and I’ve been wanting to read it for a while.

3. Writing for Developers: Blogs That Get Read by Cynthia Dunlop and Piotr Sarna.

Not ML-related, but still relevant.

4. Pen & Paper Exercises in Machine Learning by Michael U. Gutmann.

I’m not sure if I’ll go through the whole book, but it looks fun—maybe also for a study group?

5. Fundamentals of Data Engineering: Plan and Build Robust Data Systems by Joe Reis and Matthew Housley.

Not 100% ML-related, but relevant in the field.

6. Large Language Models: A Deep Dive: Bridging Theory and Practice by Uday Kamath, Ph.D.

This one is probably too long and expensive, but I want to get it haha.