Walk me through how backpropagation works.

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The short answer

Backpropagation computes the gradient of the loss with respect to every parameter by applying the chain rule backward through the network, reusing intermediate results from the forward pass. These gradients are then used by an optimizer to update the weights via gradient descent.

How to think about it

Backpropagation computes the gradient of the loss with respect to every parameter by applying the chain rule backward through the network, reusing intermediate results from the forward pass. These gradients are then used by an optimizer to update the weights via gradient descent.

Learn it properly Backprop by hand

Keep practising

What is backpropagation and how does the chain rule make it work? Walk me through the forward pass of a neural network end-to-end. In a PyTorch training loop, why do you need to call optimizer.zero_grad() before backpropagation? What is the vanishing gradient problem and how do you fix it? What is the vanishing gradient problem, and how do you address it?

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