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

For example, a neural network with 4 inputs ... and a basic understanding of neural networks but does not assume you are an expert using the back-propagation algorithm. The demo program is too long to ...

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 ...