Tech Xplore on MSN

Overparameterized neural networks: Feature learning precedes overfitting, research finds

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...
Nature

Explainability Methods for Deep Learning Neural Networks

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved remarkable ...
dbta

TigerGraph Unveils its ML Workbench to Deliver Deep Learning and Graph Neural Network Frameworks

TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench—a powerful toolkit that enables data scientists to significantly improve ML ...
Science Daily

Deep neural network to find hidden turbulent motion on the sun

Scientists developed a neural network deep learning technique to extract hidden turbulent motion information from observations of the Sun. Tests on three different sets of simulation data showed that ...
Hosted on MSN

Deep Neural Network From Scratch in Python ¦ Fully Connected Feedforward Neural Network

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning! New details in Charlie Kirk shooting as his widow breaks her silence Trump ...
VentureBeat

Deep generative models could offer the most promising developments in AI

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More This article is contributed by Rick Hao, lead deep tech partner at ...
VentureBeat

10 years later, deep learning ‘revolution’ rages on, say AI pioneers Hinton, LeCun and Li

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Artificial intelligence (AI) pioneer ...
Phys.org

Self-learning neural network cracks iconic black holes

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...