Neural Networks Explained: How They Work + Build One From Scratch

What actually is a neural network? In this video we open one up completely — from a single neuron to a working network you can build yourself in under 20 lines of Python. You'll learn: • The neuron: weights, a bias, a sum and an activation (worked example: should I take an umbrella? → 75%) • Activation functions: sigmoid and ReLU, and why they matter • Layers: input, hidden and output — and why a whole layer is one matrix multiplication • Why stacking layers without activations is pointless (pancakes ≠ cake) • The universal approximation theorem: building any curve out of "bumps" • Going deep: edges → shapes → parts → objects • How a network draws decision boundaries — one neuron = one line; many neurons = any shape • The 5-step recipe for every project: data, design, random init, train, test • A complete neural network from scratch in NumPy (17 lines), then the same thing in PyTorch • A live training demo that scores 100% on data it has never seen • The family: MLPs, CNNs, RNNs and Transformers CHAPTERS 00:00 Introduction 00:44 The Plan 01:04 01 · The Neuron 03:13 02 · Layers 05:14 03 · Making Decisions 06:20 04 · Build One From Scratch 08:55 05 · The Neural Network Family 09:49 Recap Next lesson: How Neural Networks Learn — backpropagation, step by step, with real numbers. Prerequisite (optional): The Math Behind AI. TAGS neural network, neural networks explained, what is a neural network, how neural networks work, neural network from scratch, build a neural network, neural network python, numpy neural network, pytorch tutorial, perceptron, activation function, relu, sigmoid, hidden layers, deep learning explained, universal approximation theorem, decision boundary, machine learning for beginners, AI explained HASHTAGS #NeuralNetworks #DeepLearning #MachineLearning