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

Gradient descent Taking this performance metric and pushing it back through the network is the backpropagation phase of the learning cycle, and it is the most complex part of the process.

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 ...

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 ...

Here, we propose a hardware implementation of the backpropagation algorithm that progressively updates each layer using in situ stochastic gradient descent, avoiding this storage requirement.

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 ...

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 ...

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 ...