Question

Gradient at a given layer is the product of all gradients at the previous layers.
False
True

Answer 1

Gradient in a neural network layer is the product of all gradients at the previous layer. Neural network can be taken as a set of nested functions, having each layer of a matrix multiplication followed by some non-linear function. These non-linear functions are embedded when you add layers to a neural network. By applying the chain rule, you can derive or take the gradient of a nested function. This implies that by using the gradient of the inner function you take the gradient of the outer function.

Therefore the given statement is true.

Answer 2

"Gradient at a given layer is the product of all gradients at the previous layers. The given statement is TRUE.

This is due to the fact that gradient which is present in the neural network layer is the collections of all gradients that is present in before layer. Neural network takes as a nested function that has matrix multiplication in one layer and non-linear function in other layer.

This shows when the user uses the gradient of one function that is inner side of the layer, the gradient of the outer layer is utilised. Hence, it is true."