Some free beginner courses from Google:

Introduction to Generative AI Learning Path

This learning path provides an overview of generative AI concepts, from the fundamentals of large language models to responsible AI principles.

01 Introduction to Generative AI
02 Introduction to Large Language Models
03 Introduction to Responsible AI
04 Prompt Design in Vertex AI
05 Responsible AI: Applying AI Principles with Google Cloud

Here are a few articles:

• Simplest Implementation of Diffusion Models

• Differentiable Programming from Scratch

• Exploring Neural Graphics Primitives

• Why Train When You Can Optimize?

• Solving Transformer by Hand: A Step-by-Step Math Example

• Hacker’s guide to Neural Networks

(I kind of go for the “from scratch” manner of exposition.)

Two slightly older books are pretty good, cause they cover a lot of ground, and well-understood “greatest hits” kind of problems:

• Programming Collective Intelligence
• Elegant SciPy

Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani is my highest recommended intro to machine learning text:

• https://www.statlearning.com/

It’s where I think most folks should start if they have the requisite math background. It currently has R and Python versions (I used the R version). It was actually a goal of mine at one point to write an Elixir version, but I’ve never come back to it

I also think Deep Learning by Goodfellow, Bengio, Courville is excellent if you’re more interested in Neural Networks, specifically:

• https://www.deeplearningbook.org/contents/intro.html