Neural Networks Explained Visually - From Neuron to Prediction

Neural networks explained visually - from a single artificial neuron all the way to backpropagation, gradient descent, and real-world applications like computer vision and LLMs. No prior ML background required. In this video, we build up a neural network piece by piece using one running example (predicting house prices), so every new concept - weights, bias, activation functions, layers, loss, backpropagation - connects to something concrete instead of floating in abstract math. CHAPTERS 00:00 From Decision Trees to Random Forests 00:48 It Looks Complicated - But We'll Simplify It 01:02 Build It One Piece at a Time 04:37 Meet the Artificial Neuron 06:10 Weighted Inputs Explained 08:34 Bias and Activation Functions 10:48 Connecting Neurons Into Layers 13:11 Making a Prediction 13:53 Measuring Error With Loss 15:01 Backpropagation and Gradients 16:17 Gradient Descent 17:31 Training in Batches & Generalization 20:15 Real-World Applications 21:46 Recap and What's Next 🎙️ Voice & production powered by Spyrath Studio - built by Sarath Vaddi. I create practical videos about Artificial Intelligence, Machine Learning, AI Agents, AI Security, software engineering, and emerging AI technologies - explained in simple language. 📚 Learn • Build • Impact ✍️ Follow my AI articles on Substack: sarathvaddi.substack.com #ArtificialIntelligence #MachineLearning #AI #SpyrathStudio #SarathVaddi #NeuralNetworks #DeepLearning #Backpropagation