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Neural Networks and AI / Training Deep Networks

Normalization, Residual Connections, and Stable Deep Architectures

Stacking more layers on a limit-order-book or document encoder does nothing useful if activations saturate and backpropagated deltas shrink to numerical dust. This note ties two architectural stabilisers together: normalisation layers that keep per-layer signal scale under control, and residual (skip) connections that give gradients an identity path through depth — so depth becomes a usable degree of freedom rather than a vanishing-gradient trap.

In this lesson

  1. Learning objectives
  2. Why depth needs an architectural contract
  3. Normalisation layers
  4. Residual connections
  5. Combining the pieces

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