Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material.

For example, the calculus derivative of the hyperbolic tangent function is (1 - y)(1 + y). Because the back-propagation algorithm requires the derivative, only functions that have derivatives can be ...

This deep dive covers the full mathematical derivation of softmax gradients for multi-class classification. #Backpropagation #Softmax #NeuralNetworkMath ...