Theory of Intelligence
DEEP LEARNING•Training Deep Networks•Unstable Gradients•1.5 Gradient Clipping & Skip Connections

Introduction

Two simple tools for stable deep training: capping gradients that grow too large, and adding skip connections that give gradients a shortcut through the network.

Introduction

The previous topics prevent unstable gradients by setting things up well: good initial weights, good activations, normalization. This topic adds two more tools:

  • gradient clipping is a safety net: if a gradient gets too big anyway, shrink it before taking the step;
  • residual connections change the architecture so gradients have a direct shortcut from the output to every layer.

Think of driving down a steep mountain road. Gradient clipping is a speed limiter: whatever happens, the car never goes faster than a safe maximum. Residual connections are like a tunnel straight through the mountain: the signal doesn't have to follow every hairpin bend.

Discuss theoretical concepts, mathematical formulas, and architectural foundations.

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