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 ...

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 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 ...

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 ...

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 ...

Struggling to understand how logistic regression works with gradient descent? This video breaks down the full mathematical derivation step-by-step, so you can truly grasp this core machine learning ...