Skip To Main Content
Namespace Resilient BackProp on Muck Rack

Namespace Resilient BackProp

As seen in: Visual Studio Magazine
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Namespace Resilient

Contact Namespace Resilient, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

How To Use Resilient Back Propagation To Train Neural Networks

|
By James McCaffrey, Namespace Resilient BackProp, int numHidden, int numOutput
| Visual Studio Magazine Verified
Neural Network Lab It's more complex than back propagation, but Rprop has advantages in training speed and efficiency. 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 propagation: First, training with Rprop is often faster than training with back propagation.
Open in Who Shared Issue with article?
See all 1 articles