By far the most common neural network training technique (but not necessarily the best) is to use what's called the back-propagation algorithm. Although there are many good references available that ...

Master the math behind backpropagation with a clear, step-by-step derivation that demystifies neural network training.

No one knew how to effectively train artificial neural networks with hidden layers — until 1986, when Hinton, the late David Rumelhart and Ronald Williams (now of Northeastern University) published ...

An Introduction to Neural Networks for a good in-depth walkthrough with the math involved in gradient descent. Backpropagation is not limited to function derivatives. Any algorithm that ...

Neural networks are organized in layers ... the network should have produced a value close to 1. The goal of the backpropagation algorithm is to adjust input weights so that the network will ...

Optimizers for neural networks typically use some form of gradient descent algorithm to drive the backpropagation, often with a mechanism to help avoid becoming stuck in local minima, such as ...

The most common algorithm used to train feed-forward neural networks is called back-propagation. Back-propagation compares neural network actual outputs (for a given set of inputs, and weights and ...