Phase 2: Advanced Training Techniques · ~90 minutes · Python

Learning Rate Schedules and Warmup

The learning rate is the single most important hyperparameter. Not the architecture. Not the dataset size. Not the activation function. The learning rate. If you tune nothing else, tune this.

Hiring signal: Understanding of learning rate schedules and warmup internals

What you will learn

Implement constant, step decay, cosine annealing, warmup + cosine, and 1cycle learning rate schedules from scratch
Demonstrate the three failure modes of learning rate selection: divergence (too high), stalling (too low), and oscillation (no decay)
Explain why warmup is necessary for Adam-based optimizers and how it stabilizes early training
Compare convergence speed across all five schedules on the same task and select the appropriate one for a given training budget

Introduction

Type: Build Languages: Python Prerequisites: Lesson 03.06 (Optimizers), Lesson 03.08 (Weight Initialization) Time: ~90 minutes

Learning Objectives

Implement constant, step decay, cosine annealing, warmup + cosine, and 1cycle learning rate schedules from scratch
Demonstrate the three failure modes of learning rate selection: divergence (too high), stalling (too low), and oscillation (no decay)
Explain why warmup is necessary for Adam-based optimizers and how it stabilizes early training
Compare convergence speed across all five schedules on the same task and select the appropriate one for a given training budget

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Key terms in this lesson

Learning Rate