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An explainer
Neural
Networks
Cover & TOC
The learning game
  • 01 The prediction game
  • 02 Kinds of learning
  • 03 Turning the world into numbers
  • 04 The simplest model
  • 05 Measuring wrongness
  • 06 Drawing a boundary
How learning happens
  • 07 Which way is downhill?
  • 08 Gradient descent
  • 09 Tuning the descent
  • 10 Batches, epochs & noise
From a line to a network
  • 11 Neural networks
  • 12 Why depth wins
  • 13 Backpropagation
  • 14 The training loop
  • 15 Where training starts
  • 16 When gradients vanish
  • 17 Shapes of networks
Learning vs. memorizing
  • 18 Learning vs. memorizing
  • 19 Data quality & the three splits
  • 20 Judging a model
The bigger picture
  • 21 A short history
  • 22 Embeddings
  • 23 From this loop to an LLM
§ Glossary
Section 20

Judging a model

Why the loss is not the metric you care about

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Data quality & the three splits
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A short history
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