Generative AI Episode 04: How Neural Networks Work

How do neural networks learn to recognize images, understand speech and detect complex patterns? In Episode 4 of our beginner-friendly Generative AI series, we explain how neural networks work—without complicated mathematics or technical jargon. Using relatable examples such as student admission decisions, recognizing a cat and detecting suspicious bank transactions, this video breaks down the key building blocks of a neural network. In this video, you’ll learn: • What an artificial neuron is • How inputs and outputs work • What weights and bias mean • What an activation function does • How input, hidden and output layers work • How neural networks learn from mistakes • What loss, backpropagation and gradient descent mean • Why neural networks are not digital human brains No programming experience, advanced mathematics or previous AI knowledge is required. This video is part of an end-to-end Generative AI series covering Machine Learning, neural networks, Transformers, Large Language Models, prompt engineering, embeddings, vector databases, RAG, AI agents, tools, MCP, evaluation, guardrails, deployment and AI governance. ▶️ Previous episode: AI vs Machine Learning vs Deep Learning vs Generative AI ▶️ Next episode: How Transformers and Attention Work Subscribe and follow the complete journey from AI beginner to AI architect. #NeuralNetworks #DeepLearning #GenerativeAI #AIForBeginners #MachineLearning #ArtificialIntelligence #LearnAI #AITutorial