How does a neural network with millions of parameters actually learn from its mistakes? This episode dives under the hood of deep learning's core engine, demystifying the two algorithms that make it all possible: Gradient Descent and Backpropagation. We'll use intuitive analogies to explain how AI navigates a vast mathematical landscape to find the answers that minimize its errors.
