Resilient Backpropagation (Rprop): The Robust Optimization Algorithm for Training Deep Neural Networks

Introduction

Resilient Backpropagation (Rprop) is a popular optimization algorithm used in training artificial neural networks. The algorithm was first introduced by Martin Riedmiller and Heinrich Braun in 1993 and has since been widely adopted due to its effectiveness in training deep neural networks.

In this article, we will discuss the basics of Rprop, how it works, and its advantages over other optimization algorithms.

The Basics of Backpropagation

Before diving into the specifics of Rprop, ...