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.
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: Linear Algebra Intuition
- Time: ~60 minutes
Objective
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
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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