SanDiegoMachineLearning/bookclub

What this project is

This is the book club repository for the San Diego Machine Learning (SDML) meetup group. It holds notes, links, slides, and videos from the group's book club meetings, where members read and discuss machine-learning-related books and courses together.

It is not the repository for general ML talks — the README explicitly points non-book talks to a separate SDML talks repo.

What's inside

The README lists many past and ongoing reading series, each with its own linked document (e.g., ai-engineering.md, hands-on-machine-learning-3.md) or external links. Each entry names the book or course, sometimes when the series started, and where to find its materials.

Notable entries include:

• AI Engineering (applications with foundation models) — notes/slides and videos.
• LLM Inference Illustrated — video recordings; started March 2026.
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition — started August 2025.
• Computer Vision — schedule, notes/slides, recordings, extra materials (two linked book covers shown).
• Mathematics for Machine Learning — schedule, notes/slides, recordings, extras.
• Understanding Deep Learning — schedule, notes/slides, recordings, extras.
• Generative AI with Large Language Models (LLMs) — a DeepLearning.AI/Coursera course; this entry has direct links to kickoff and weekly discussion slides and YouTube videos (no separate .md file).
• Probabilistic Machine Learning: Advanced Topics — started January 2023.
• Learning SQL Queries (book: Sams Teach Yourself SQL in 10 Minutes) — started September 2022.
• Quantum Computing (book: Dancing with Qubits) — started August 2022.
• Machine Learning with Graphs — started January 2022.
• Hands-On Machine Learning …, 2nd Edition — started October 2021.
• Reinforcement Learning: An Introduction — notes/slides and videos.
• Algorithms to Live By — slides are in a different personal repo (Ted's talks), video on YouTube.
• Designing Data-Intensive Applications — notes and videos.
• Deep Learning with PyTorch — notes and videos.
• Feature Engineering for Machine Learning — points to companion Jupyter notebooks in an external repo (alicezheng/feature-engineering-book) plus a repo with dataset-download instructions (tedkyi/feature-engineering).
• Hands-On ML 2nd Edition (2019) — points to the author's public notebooks repo (ageron/handson-ml2).
• Natural Language Processing in Action — points to companion code repo (totalgood/nlpia).

How it is organized

Each book/course typically gets a single Markdown document in the repo (e.g., ai-engineering.md) containing that series' schedule, slides, recordings, and additional materials. The README acts as an index page with book-cover images that link to each document.

Some entries have no document in this repo — instead, they link externally (Google Slides, YouTube, GitHub repos).

Community / contact info

The bottom of the README tells readers how to get involved:

• Join the SDML Meetup group for event announcements.
• Join the SDML Slack channel for events, job postings, and discussion (a shared invite link is provided).
• See the SDML talks repo for (non-book) ML talk slides and videos.

Notes on facts

I have not invented any content beyond what the README states. For instance, the README does not explain what the book club actually does during meetings (beyond naming "sessions" and "discussions"), does not state membership size, and does not include dates for most series beyond the ones explicitly mentioned. I have kept to what is written.