Theory of Intelligence
DEEP LEARNING•Neural Networks•Mlps For Regression And Classification•3.3 Choosing Losses & Output Layers

Introduction

A practical decision table for output layers and loss functions, plus how to handle imbalanced classes with weighted losses and other tricks.

Introduction

Choosing the output layer and the loss is the most important design decision after picking the architecture. Get it wrong and the network optimises the wrong thing — sometimes without any error message, just mysteriously bad results.

The loss function is the network's definition of success. It's like the scoring rules of a game: change the rules and players change their strategy. Choose a loss that rewards exactly what you care about.

Discuss theoretical concepts, mathematical formulas, and architectural foundations.

Supports inline code with `backticks`
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