RAI Crash Course: Neural Networks, XOR & the AI Winter | Module 1, Ch 2

How an artificial neuron works (inputs, weights, bias, activation), the perceptron, why it could not learn XOR, and how that led to the first AI winter. Then hidden layers, backpropagation, the deep learning boom, activation functions (sigmoid, tanh, ReLU), and why neural networks are powerful but opaque. Animated, from zero. ⏱ Chapters 00:00 Neural networks: from one neuron to deep learning 00:43 The artificial neuron 01:32 Animation: The perceptron, XOR and the AI winter 03:00 Example: why XOR needs a hidden layer 03:54 The neural network timeline 04:58 Why the activation function matters 05:41 The price: graceful, but opaque 06:16 Easy to mix up 07:29 Recap 📚 Full course: see the RAI Crash Course playlist on this channel. ▶ Next: Module 1, Chapter 3: How Machines Learn (and Fail) 🎬 Course trailer:    • RAI Crash Course: Risk & AI, Animated | Co...   ⚠ Unofficial: this is an independent study guide. It is not affiliated with, endorsed by or sponsored by GARP. "RAI" refers to the Risk and AI certificate only to describe what the material helps you study. All explanations, examples and questions are original and written in our own words; always study from the official curriculum. 🤖 AI disclosure: the narration is an AI-generated (synthetic) voice, and the visuals are generated programmatically. Scripts are human-reviewed. No real exam questions are used or disclosed. #RAI #RiskAndAI #MachineLearning #AIExplained