Reading list
The references I point people to when they want the full version of the explainers above.
Books
- Deep Learning — Goodfellow, Bengio & Courville. The standard reference; free at deeplearningbook.org.
- Understanding Deep Learning — Simon Prince. Modern, visual; free PDF at udlbook.github.io.
- The Elements of Statistical Learning — Hastie, Tibshirani & Friedman. Trees, boosting, the statistical view; free PDF.
- Pattern Recognition and Machine Learning — Bishop. The probabilistic foundations.
Courses & sites
- Dive into Deep Learning — interactive book with runnable code, d2l.ai.
- fast.ai — practical deep learning, top-down.
- Distill.pub — visual explanations of attention, momentum, and more.
- The Illustrated Transformer — Jay Alammar’s walk-through of attention.
These notes are a personal reference and will grow over time. Corrections welcome — contact details are on the home page.