In the previous chapter, we learned various strategies to guide AI models 'down the mountain' (optimization algorithms), such as SGD and Adam. The core of these strategies relies on a key piece of ...

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 ...

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 ...

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 ...

The hype over Large Language Models (LLMs) has reached a fever pitch. But how much of the hype is justified? We can't answer that without some straight talk - and some definitions. Time for a ...

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 ...

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 ...

Let's explore mini-batch training, the third among a variety of back-propagation algorithms you can use for training a neural network. The most common technique used to train a neural network is the ...