Backpropagation sounds like “sending errors backward.”

But that explanation is too vague.

The real question is:

How does a neural network know which weights caused the error?

That is what backpropagation solves.

Core Idea

Backpropagation is the mechanism that tells each parameter how much it contributed to the final loss.

A neural network first makes a prediction.

Then it measures the error.

Then it sends gradient information backward through the network.

That gradient tells the optimizer how to update the weights.

The Key Structure

The full training flow looks like this:

Input → Forward Propagation → Prediction → Loss → Backpropagation → Gradients → Weight Update

In simple terms:

Forward pass computes the output

Loss measures the error

Backpropagation computes gradients

Optimizer updates parameters

The forward pass answers:

“What did the model predict?”

The backward pass answers:

“What should change to reduce the error?”

Implementation View

At a high level, training works like…