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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 23
Interpretability
23.1
Why look inside
23.2
Probing
23.3
The logit lens and the residual stream
23.4
Circuits
23.5
Superposition
23.6
Sparse autoencoders
23.7
Steering and representation engineering
23.8
Attribution graphs and open problems