Back propagation requires a value for a parameter called the learning rate. The effectiveness of back propagation is highly sensitive to the value of the learning rate. Rprop was developed by ...

In early December, dozens of alternatives to traditional backpropagation were proposed during a workshop at the NeurIPS 2020 conference, which took place virtually.

Machine-learning algorithms find and apply patterns in data. ... To clear things up, I drew you this flowchart on the back of an envelope so you can work out whether something is using AI or not.

Resilient back propagation (Rprop), an algorithm that can be used to train a neural network, is similar to the more common (regular) back-propagation. But it has two main advantages over back ...