Understanding Backpropagation and Gradient Descent in Neural Networks
Suppose you're building a neural network, maybe even a deep and complex one. You've set up the layers, initialized weights, and defined activation functions. But here's a question: Can this neural network make accurate predictions without tuning? The short answer: No. Building a neural network is just the start, the real magic lies in fine-tuning its parameters (i.e., weights and biases) so it actually learns from the data.
This technical deep-dive breaks down the mathematical backbone of neural network optimization. It explains why primitive weight-guessing methods fail and explores how calculus provides a deterministic solution. The article mathematically deconstructs how backpropagation computes loss gradients via the chain rule, and how Gradient Descent utilizes learning rates to iteratively update weights and biases to minimize error.