What is Backpropagation?

Backpropagation is the process that lets a neural network learn from its own mistakes. It works by propagating errors backward, computing gradients using the chain rule, and updating weights and biases to improve performance.

In short, the network makes a guess, measures how wrong it was, and adjusts itself accordingly. This is the core mechanism that allows neural networks to actually improve with practice.

How Neural Networks Learn from Errors

After the network makes a prediction, it compares the result with the correct answer to calculate the error. It then works backward through the network to find out which weights and biases caused the error and updates them to improve future predictions.

By repeating this process many times during training, the network gradually becomes more accurate.

The Role of Gradients

A gradient basically tells the network which direction and how much to adjust each weight to reduce its error. Mathematically speaking, backpropagation works backward from the output to efficiently calculate the gradient of the loss function, a vector of derivatives for every equation in the network.

This gradient tells optimization algorithms such as gradient descent which equations to adjust, and which direction to adjust them in, to reduce loss. Without gradients, the network would have no clue which of its thousands of weights actually caused the error, or how to fix it.