Phase 1: Training Neural Networks · ~75 minutes · Python

Regularization

Your model gets 99% on training data and 60% on test data. It memorized instead of learning. Regularization is the tax you impose on complexity to force generalization.

What you will learn

Implement dropout with inverted scaling, L2 weight decay, batch normalization, layer normalization, and RMSNorm from scratch
Measure the train-test accuracy gap and diagnose overfitting using regularization experiments
Explain why transformers use LayerNorm instead of BatchNorm and why modern LLMs prefer RMSNorm
Apply the correct combination of regularization techniques based on the severity of overfitting

Introduction

Type: Build
Languages: Python
Prerequisites: Optimizers
Time: ~75 minutes

Objective

Learning objectives

Implement dropout with inverted scaling, L2 weight decay, batch normalization, layer normalization, and RMSNorm from scratch
Measure the train-test accuracy gap and diagnose overfitting using regularization experiments
Explain why transformers use LayerNorm instead of BatchNorm and why modern LLMs prefer RMSNorm
Apply the correct combination of regularization techniques based on the severity of overfitting

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.

Key terms in this lesson

Overfitting