AI Engineering/Phase 03/Lesson 07

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.

Build 1.3 hr Python

Prerequisites: Lesson 03.06 (Optimizers)

#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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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.