Build your own backpropagation algorithm from scratch using Python — perfect for hands-on learners!

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 ...

Hinton's FF algorithm replaces the forward-backward passes of backpropagation training with ... the network is given a generated negative example that is not taken from the dataset.

The connection weights between these layers are trained by the backpropagation algorithm while minimizing a specific cost function. This framework happens to provide state-of-the-art results ...

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 ...