How do I implement backpropagation in neural networks?

How do I implement backpropagation in neural networks?

Backpropagation is a method used to calculate the gradient of the loss function with respect to the weights of the network. Would you like a step-by-step guide?

Yes, please provide the steps.

1. Initialize weights randomly. 2. Perform a forward pass to compute the output. 3. Calculate the loss. 4. Compute the gradients using the chain rule. 5. Update the weights using the gradients.

What is the chain rule?

The chain rule is a fundamental theorem in calculus that allows us to compute the derivative of a composite function. In backpropagation, it's used to find how changes in weights affect the loss.

Can you give an example of how to calculate gradients?

Sure! If you have a simple neural network with one hidden layer, you would calculate the gradient of the loss with respect to each weight by applying the chain rule through each layer.