umass

Gradient Descent and Neural Network

In this talk, I would like to briefly discuss gradient descent and its variations. We will talk about the concept of gradient descent and then apply the method to neural networks. We will keep it as ...

Why Can AI Learn? How Neural Networks Work [AI System Course Part 2]

What you will learn in this article The contents of a neural network (artificial neurons, layers, weights and biases, ...
umass

Coarse Gradient Descent Method and Quantization of Deep Neural Networks

Quantization is an effective approach to accelerate deep neural networks by restricting their weights and activation functions to low precisions. However, the training objective (loss function) ...

[G-Certification Day 14] How does AI find its own mistakes? "Backpropagation"

Hello everyone, stay safe!! This is Takachan the engineer.How does AI find its own mistakes and make its next predictions more accurate?Humans review the questions they got wrong on a test and study ...
InfoWorld

Styles of machine learning: Intro to neural networks

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 ...
GitHub

Understanding and mitigating gradient pathologies in physics-informed neural networks

Contribute to PredictiveIntelligenceLab/GradientPathologiesPINNs development by creating an account on GitHub.
BIOENGINEER

COSMOS Learns Neural Representations as Gauge Dynamics on the Grassmannian

The COSMOS framework trains orthonormal subspace frames with conjugation-based gauge-style updates, holding geometric ...