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

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 ...

Hinton's motivation for the algorithm is to address some of the shortcomings of standard backpropagation training which requires full knowledge of the computation in the forward pass to compute ...

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.

It is a mathematical method for training neural networks to recognize patterns in data. The history and development of the backpropagation algorithm, including the contributions of Paul Werbos, take ...

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...

Dr Hinton popularised a clever mathematical algorithm known as backpropagation to solve this problem in artificial neural networks. But it was long thought to be too unwieldy to have evolved in ...