What is backpropagation?
Backpropagation is the main method used to teach neural networks. It tells the network how to change its internal numbers (weights) to make better predictions. Think of it as a feedback mechanism: the network makes a guess, checks how wrong it was, and uses that information to improve.
Start with a forward pass: the network takes inputs and produces an output. If the output is wrong, we compute a loss (a number showing how wrong the output is). The goal is to reduce this loss.
Backpropagation works by computing how the loss changes when each weight changes. This is called the gradient. The gradient tells us which direction will reduce the loss.
If the gradient is positive, decreasing the weight will reduce the loss; if negative, increasing the weight will help. We combine this information with a learning rate so the updates are small and stable.
The “back” in backpropagation means we compute these gradients starting at the output and moving towards the input layers, layer by layer, applying the chain rule from calculus. You don’t need to master the calculus to understand the concept — it’s a mechanical way to give blame to every weight for the final error.
Backpropagation is repeated many times across many examples (called epochs). With each pass the weights move slightly toward values that reduce error on the training data.
There are practical tricks that make backpropagation work well in real systems: using mini-batches of data (not just one example at a time), normalizing inputs, choosing adaptive optimizers like Adam, and using regularization techniques to avoid overfitting.
A common problem is exploding or vanishing gradients — when gradients become too large or too small — which slows or prevents learning. Solutions include better initialization, normalization layers, and special architectures (like skip connections).
If you are learning by doing, implement a tiny network in code (e.g., NumPy) with one hidden layer and step through forward and backward calculations for a single example. That hands-on view makes the algorithm much clearer.
Below is a placeholder diagram: replace with an image that shows a forward pass (left-to-right) and backward pass (right-to-left) with arrows indicating gradients.
Quick checklist
- Forward pass: compute predictions.
- Loss: measure error.
- Backward pass: compute gradients and update weights.