Backpropagation Explained — How Neural Networks Actually Learn
About this lesson
How does a neural network actually learn? Not the hand-wave answer — the real one. In this video we derive backpropagation from scratch, work through the math step by step, and build it twice in code: once in raw NumPy so you see every gradient, then again in PyTorch so you see how autograd does it for you. By the end you'll understand the one algorithm running underneath every modern AI system — ChatGPT, Stable Diffusion, AlphaFold, all of them. No hand-waving. Every step shown. 🔔 Subscribe for the full series: www.youtube.com/@UCTf4vbJPhLrtjhdu2q7AacA This is Chapter 7 ( Neural Network Fundamentals) Part 2 📚 WHAT YOU'LL LEARN ✅ How loss functions measure "wrongness" with one number ✅ Why gradient descent is just rolling downhill — and the math behind it ✅ The chain rule, derived and applied (no calculus background needed) ✅ Backpropagation step-by-step on a single neuron ✅ Vectorized backprop formulas for any number of layers ✅ Why deep networks were impossible to train for decades ✅ How modern fixes (ReLU, batch norm, residual connections) solved it ✅ Building backprop from scratch in NumPy (no framework) ✅ Doing the same thing in PyTorch with autograd ❤️ SUPPORT THE CHANNEL If this video helped a concept finally click for you, the best thing you can do is hit the like button — it tells YouTube to show this to other people who need it. Subscribing means you'll see Episode 3 when it drops. #backpropagation #neuralnetworks #deeplearning #machinelearning #ai #pytorch #python #datascience #gradientdescent #chainrule What is backpropagation, how does backpropagation work, neural network training, gradient descent explained, chain rule neural networks, backpropagation from scratch, backpropagation Python tutorial, PyTorch autograd explained, neural network math, deep learning tutorial.
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