University of Delaware

Star power

The neural network approach uses multiple or “deep” layers that learn to identify increasingly complex features in data. The ...
Hosted on MSN

Network in Network (NiN) Explained – Deep Neural Network Tutorial with PyTorch

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
Hosted on MSN

Backpropagation from Scratch in Python – Step by Step Neural Network Tutorial

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...
Frontiers

Convolutional neural networks decode finger movements in motor sequence learning from MEG data

1 Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia 2 Department of Learning, Data Analytics and Technology, Section Cognition, Data and ...
pv magazine International

PV module fault diagnosis uses convolutional neural network

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...
IEEE

A Tutorial on Quantum Convolutional Neural Networks (QCNN)

Abstract: Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn ...
C&EN

pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China Yangtze Delta Region Institute (Quzhou), University of Electronic ...
EurekAlert!

D2-GCN: a graph convolutional network with dynamic disentanglement for node classification

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...