AI Foundations for Software Engineers
A streamlined guide for busy professionals
AI needs more software engineers.
This newsletter was started three years ago based on that premise, and it has only become more true since. To make AI useful and accessible, we need ways to apply it economically and efficiently to real-world applications. That’s an engineering task.
I’ve updated the ML roadmap to be an AI foundations guide for software engineers. I’ve spent the past few months collecting and analyzing AI educational resources, and I’ve landed on the best resources for any engineer wanting a foundational understanding of AI.
The guide contains 5 sections:
General knowledge resources including books on the differences between research and engineering and a philosophy of software design.
ML foundations resources including math and hands-on general machine learning exercises.
LLM resources including transformers, building an LLM from scratch, and post-training.
ML/AI engineering resources including designing AI applications, designing ML systems, inference engineering, and more.
Hands-on guides that help you get more involved with important technologies and topics in AI. This section will build up as I release more of these as part of the newsletter.
The biggest changes from the previous guide are:
A greater focus on the knowledge needed to actually start building.
A more streamlined collection with fewer—but deeper—resources.
No separate free and paid tracks—just the resources I consider the best.
No prerequisite section. Familiarity with at least one programming language is assumed.
Please check it out at learn.aiforswes.com! I’d greatly appreciate your feedback—especially if a topic is missing, a resource should be reconsidered, or part of the learning path is unclear.
Thanks for reading!
Always be (machine) learning,
Logan