Phase 0: Neural Network Foundations · ~90 minutes · Python

Multi-Layer Networks and Forward Pass

One neuron draws a line. Stack them, and you can draw anything.

What you will learn

Build a multi-layer network from scratch with Layer and Network classes that perform a complete forward pass
Trace matrix dimensions through each layer of a network and identify shape mismatches
Explain how stacking nonlinear activations enables a network to learn curved decision boundaries
Solve the XOR problem using a 2-2-1 architecture with hand-tuned sigmoid weights

Introduction

Type: Build
Languages: Python
Prerequisites: The Perceptron
Time: ~90 minutes

Objective

Learning objectives

Build a multi-layer network from scratch with Layer and Network classes that perform a complete forward pass
Trace matrix dimensions through each layer of a network and identify shape mismatches
Explain how stacking nonlinear activations enables a network to learn curved decision boundaries
Solve the XOR problem using a 2-2-1 architecture with hand-tuned sigmoid weights

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.

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