An illustrated field guide

Deep Learning
Math

The mathematics behind machine learning — rebuilt from intuition, for anyone who was ever made to feel they weren't a “math person.” You are. You just needed the story.

CONTENTS

Every chapter starts with a human problem, not a formula. You build the idea with pictures and your own hands first. The symbols arrive last — by then they're old friends.

Intuition before notation The real history, woven in Touch it, don't just read it
The Chapters
CHAPTER 01
Trigonometry

A spinning point becomes a wave. How we learned to measure the unreachable — and the mistranslation that named the sine.

Read chapter →
CHAPTER 02
Calculus

Sneak up on an instant to find its rate of change; add up infinity to find a total. The two moves behind how machines learn.

Read chapter →
CHAPTER 03
Differential Equations

Follow a field of arrows to thread the future out of a rule. The same idea an AI uses to turn noise into an image.

Read chapter →
CHAPTER 04
Vectors & Dot Products

Turn meaning into an arrow, and measure how alike any two things are. The literal engine of attention.

Read chapter →
CHAPTER 05
Matrices

A grid of numbers that grabs space and moves it. A neural network is a tall stack of these.

Read chapter →
CHAPTER 06
Probability

Be exact about what you don't know. Watch a model pick its next word, and meet Bayes' rule.

Read chapter →
The capstone
CHAPTER 07 · CAPSTONE
Build an LLM from Scratch

Watch all six ideas snap together into a working language model — tokens, embeddings, attention, layers, and training, end to end.

Assemble the machine →
Hands-on labs
LAB · BUILD
Tiny Language Model Lab

Train a real bigram model live in your browser — the simplest language model. Watch the loss fall, then make it babble.

Open the lab →
LAB · ATTENTION
Tiny Transformer Lab

A real miniature GPT with working self-attention — train it and watch an attention head form, live.

Open the lab →
LAB · CIRCUIT
The Induction Head

Train a 2-layer model on repeats and watch it grow the famous pattern-copying circuit behind in-context learning.

Open the lab →
LAB · DECODE
Reverse-Engineer a Mind

Open the trained model and read the algorithm it wrote for itself — an intro to mechanistic interpretability.

Open the lab →