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The book
Chapters
0
The Toolkit
›
1
Embeddings
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2
Tokenization
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3
From Static to Contextual Meaning
›
4
Attention from First Principles
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5
The Transformer Block
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6
Build a GPT from Scratch
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7
Encoders
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8
Pretraining Recipes
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9
Scaling Laws
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10
Distributed Training
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11
GPU Programming for ML
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12
FlashAttention and Efficient Attention
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13
Mixture of Experts
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14
Post-training and Supervised Fine-tuning
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15
Reinforcement Learning Foundations for LLMs
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16
Reinforcement Learning from Human Feedback
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17
Direct Preference Optimization
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18
Reasoning Models and RL with Verifiable Rewards
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19
Alignment and Safety
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20
Fast
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21
State-Space and Linear-Time Models
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22
Evaluation
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23
Interpretability
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24
Tool Use and Agents
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25
Memory and Long-Running Agents
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26
Self-Improvement Loops
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27
Images as Tokens
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28
Self-supervised Vision
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29
Video Representations
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30
Joint Text-Image Embeddings
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31
Vision-Language Models
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32
Autoencoders
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33
Diffusion and Flow Matching
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34
Image and Video Generation at Scale
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35
How Sound Becomes Vectors
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36
Speech to Text
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37
Text to Speech
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All chapters
Chapter 1
Embeddings
1.1
Why computers need numbers for things
1.2
An embedding is a lookup table
1.3
The geometry of meaning
1.4
You shall know a word by the company it keeps
1.5
Learning embeddings with word2vec
1.6
Looking inside an embedding space
1.7
Embeddings are everywhere
1.8
Embeddings from encoders
1.9
Where static embeddings break
1.10
The embedding map