Notes and links from the SDML book club meetings. For slides and videos from other, non-book machine learning talks, please see the SDML talks repo.
The schedule, notes/slides, recordings, and additional materials for the meetup sessions for Understanding Deep Learning can be found in the document understanding-deep-learning.md.
SDML is going through this new course by DeepLearning.AI on Coursera
- Kickoff meeting slides and video
- Week 1 discussion slides and video
- Week 2 discussion slides and video
- Week 3 slides and video
Materials for the book reading group for the book Probabilistic Machine Learning: Advanced Topics, which started in January 2023, can be found in the document probabilistic-ml.md.
Notes/slides, videos, schedule, and additional materials for the meetup sessions for the Learning SQL series, using the book Sams Teach Yourself SQL in 10 Minutes, which started in September 2022, can be found in the document sql-queries.md.
Videos, schedule, and additional materials for the meetup sessions for the Quantum Computing series, using the book Dancing with Qubits, which started in August 2022, can be found in the document quantum-computing.md.
Notes/slides and videos from the meetup sessions for Machine Learning with Graphs, which started in January 2022, can be found in the document machine-learning-with-graphs.md.
Notes/slides and videos from the meetup sessions for the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, which started in October 2021, can be found in the document hands-on-machine-learning.md.
Notes/slides and videos from the meetup sessions for the book Reinforcement Learning: An Introduction can be found in the document reinforcement-learning.md.
For the book Algorithms to Live By, the slides are in Ted's talks repo, and the video is on YouTube.
Notes and videos from the meetup sessions for the book Designing Data-Intensive Applications can be found in the document designing-data-intensive-apps.md.
Notes and videos from the meetup sessions for the book Deep Learning with PyTorch can be found in the document deep-learning-with-pytorch.md.
The book Feature Engineering for Machine Learning by Alice Zheng & Amanda Casari comes with a set of Jupyter notebooks so that you can run the code examples in the book. The notebooks for the book are located in the GitHub repository https://github.com/alicezheng/feature-engineering-book.
The notebook with instructions and code for downloading the datasets not contained in the book repo is located in this repo https://github.com/tedkyi/feature-engineering.
Jupyter notebooks with code from Hands-On Machine Learning is available in this repo: https://github.com/ageron/handson-ml2.
Code for Natural Language Processing in Action by Hobson Lane et al. is available on GitHub: https://github.com/totalgood/nlpia.
For slides and videos from machine learning talks, please see the SDML talks repo.
To stay in touch with San Diego Machine Learning and receive announcements of all of our events, join our Meetup group https://www.meetup.com/San-Diego-Machine-Learning.
For more events, job postings, and discussion of machine learning, join our slack channel https://join.slack.com/t/sdmachinelearning/shared_invite/zt-2b2207qhg-Iyys1g0Ot6iErTYMioV9Mg