Phase 0: Neural Network Foundations · ~60 minutes · Python

The Perceptron

The perceptron is the atom of neural networks. Split it open and you find weights, a bias, and a decision.

Hiring signal: Understanding of the perceptron internals

What you will learn

Implement a perceptron from scratch in Python, including the weight update rule and step activation function
Explain why a single perceptron can only solve linearly separable problems and demonstrate the XOR failure case
Construct a multi-layer perceptron by composing OR, NAND, and AND gates to solve XOR
Train a two-layer network with sigmoid activation and backpropagation to learn XOR automatically

Introduction

Type: Build Languages: Python Prerequisites: Phase 1 (Linear Algebra Intuition) Time: ~60 minutes

Learning Objectives

Implement a perceptron from scratch in Python, including the weight update rule and step activation function
Explain why a single perceptron can only solve linearly separable problems and demonstrate the XOR failure case
Construct a multi-layer perceptron by composing OR, NAND, and AND gates to solve XOR
Train a two-layer network with sigmoid activation and backpropagation to learn XOR automatically

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Backpropagation