In the previous chapter, we learned various strategies to guide AI models 'down the mountain' (optimization algorithms), such ...

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

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. The ...

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini Batch ...

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks. Artificial intelligence ...

In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the ...

The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult computational problem. Many aspects of modern applied research ...

In the 1960s, academics including Virginia Polytechnic Institute professor Henry J. Kelley, Stanford University’s Arthur E. Bryson, and Stuart Dreyfus at the University of California, Berkeley arrived ...