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.

Hiring signal: Understanding of regularization internals

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: Lesson 03.06 (Optimizers) Time: ~75 minutes

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

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Key terms in this lesson

Overfitting