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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
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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 35
How Sound Becomes Vectors
35.1
What sound is
35.2
The Fourier transform and the STFT
35.3
Mel spectrograms and MFCCs
35.4
Learned audio encoders, wav2vec 2.0
35.5
HuBERT and masked prediction
35.6
Audio understanding tasks