Deep Learning with Yacine on MSN

Stochastic Depth for Neural Networks – Explained Clearly

A simple and clear explanation of stochastic depth — a powerful regularization technique that improves deep neural network ...
Tech Xplore

VFF-Net algorithm provides promising alternative to backpropagation for AI training

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn ...
CNET

Apple Boosts M5 With New Neural, GPU Architecture

The chip will improve overall performance in upgraded MacBook Pros and iPad Pro, especially for graphics and AI.
Frontiers

Spiking Neural Networks at Scale: Learning Algorithms, Architectures, and Real-World Applications

Spiking Neural Networks (SNNs) are a cutting-edge approach to artificial intelligence, designed to emulate the brain's architecture and functionality. Their ...
Deep Learning with Yacine on MSN

Understanding Forward Propagation in Neural Networks with Python – Step by Step

Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs ...
GitHub

GitHub - Speedtrust/test

2017-07-06 Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. A paper by Pranav Rajpurkar, Awni Y. Hannun et al. that uses a 34-layer convolutional neural network which maps a ...
C&EN

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction

Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
IEEE

Parametric Activation Functions for Neural Networks: A Tutorial Survey

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...
IEEE

Adaptive Gradient Neural Networks for Solving the Time-Varying Sylvester Equation

Abstract: This paper develops several new dynamical designs, based on the gradient neural network (GNN), from the perspective of control theory to solve the time-varying Sylvester equation (TVSE). We ...