How does backpropagation work in neural networks?

How does backpropagation work in neural networks?

Backpropagation is a method used to calculate the gradient of the loss function with respect to the weights of the network. It involves a forward pass to compute the output and a backward pass to update the weights.

Can you explain the forward and backward passes in more detail?

Sure! In the forward pass, inputs are fed through the network to generate an output. In the backward pass, the error is calculated and propagated back through the network to update the weights using gradient descent.

What role does the learning rate play in backpropagation?

The learning rate determines the size of the steps taken towards the minimum of the loss function. A small learning rate may lead to slow convergence, while a large one can cause overshooting.

Are there any visual tools to help understand backpropagation?

Yes! There are several visualizations available online that illustrate how backpropagation works, showing the flow of gradients and weight updates.