Books
Full books to read inside the platform — Ada reads along with you, so you can highlight any passage and ask about it. 2 books · 170k words · available offline.
Read in full
Neural Networks and Deep Learning
Michael Nielsen · 2015
The gentlest real introduction to how neural nets actually learn.
Dive into Deep Learning
Zhang, Lipton, Li & Smola · 2023
Every idea paired with runnable PyTorch, from tensors to CNNs.
These are complete, openly-licensed books, ingested with their licences intact and served from your own machine. Each book page carries its licence and a link to the authors’ original — please support the authors there.
PDF library
The exact linear algebra, calculus and probability ML needs — no more, no less.
The friendliest serious introduction to statistical learning.
ISLR's rigorous older sibling — the reference you graduate into.
The book that defined the field, written to be taught from.
Where the optimisation behind every training loop is made precise.
Not a book to read — a lookup table for every matrix identity you'll need.
These open in a reader with Ada beside them, but they are framed from the authors’ own sites rather than copied here — so Ada cannot read their pages, and will tell you so instead of guessing. Quote a passage to her and she can work through it with you.