How does backpropagation use tensors?

Can you explain how backpropagation uses tensors in neural networks?

Sure! Backpropagation is an algorithm used to train neural networks, and it relies heavily on tensors to represent data and gradients.

What are tensors, and why are they important for backpropagation?

Tensors are multi-dimensional arrays that can hold data in various shapes. They are crucial for backpropagation because they allow efficient computation of gradients across the network.

How does the gradient calculation work with tensors during backpropagation?

During backpropagation, the gradients of the loss function are calculated with respect to each tensor, allowing the model to update weights effectively.

Can you give an example of how tensors are used in a simple neural network?

Absolutely! In a simple neural network, input data is represented as a tensor, and as it passes through layers, tensors are used to store weights and biases, facilitating the forward and backward passes.