Phase 3: Frameworks & Debugging · ~90 minutes · Python

Debugging Neural Networks

Your network compiled. It ran. It produced a number. The number is wrong and nothing crashed. Welcome to the hardest kind of debugging -- the kind where there is no error message.

What you will learn

Diagnose common neural network failures (NaN loss, flat loss curve, overfitting, oscillation) using systematic debugging strategies
Apply the "overfit one batch" technique to verify that your model architecture and training loop are correct
Inspect gradient magnitudes, activation distributions, and weight norms to identify vanishing/exploding gradient problems
Build a debugging checklist that covers data pipeline, model architecture, loss function, optimizer, and learning rate issues

Introduction

Type: Build
Languages: Python, PyTorch
Prerequisites: Loss Functions, Optimizers
Time: ~90 minutes

Objective

Learning objectives

Diagnose common neural network failures (NaN loss, flat loss curve, overfitting, oscillation) using systematic debugging strategies
Apply the "overfit one batch" technique to verify that your model architecture and training loop are correct
Inspect gradient magnitudes, activation distributions, and weight norms to identify vanishing/exploding gradient problems
Build a debugging checklist that covers data pipeline, model architecture, loss function, optimizer, and learning rate issues

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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.

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