GitHub

Simple Neural Network Models

This project contains implementations of simple neural network models, including training scripts for PyTorch and Lightning frameworks. The goal is to provide a modular, easy-to-understand codebase ...
Hosted on MSN

20 Activation Functions in Python for Deep Neural Networks – ELU, ReLU, Leaky-ReLU, Sigmoid, Cosine

Explore 20 different activation functions for deep neural networks, with Python examples including ELU, ReLU, Leaky-ReLU, Sigmoid, and more. #ActivationFunctions #DeepLearning #Python Tropical Storm ...
IEEE

FPGA Implementation of PoolFormer Network Using Python-Driven High-Level Synthesis Framework for Edge-AIoT Speech Recognition

Abstract: This brief presents an edge-AIoT speech recognition system, which is based on a new spiking feature extraction (SFE) method and a PoolFormer (PF) neural network optimized for implementation ...
Hosted on MSN

Build A Deep Neural Network From Scratch In Python — No Tensorflow!

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...
gc.cuny

Programming Neural Networks in Python: Part II

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...
Frontiers

Sparse connectivity enables efficient information processing in cortex-like artificial neural networks

Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse ...
GitHub

Hands-On Graph Neural Networks Using Python

Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help ...
Scientific Research Publishing

Efficient Identification Method of Interbeds Based on Neural Network Combined with Grey Relational Analysis

1 School of Earth Science and Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China. 2 Shaanxi Provincial Key Laboratory of Petroleum Accumulation Geology, Xi’an, Shaanxi, China. The storage ...
IEEE

Targeted Discrepancy Attacks: Crafting Selective Adversarial Examples in Graph Neural Networks

Abstract: In this study, we present a novel approach to adversarial attacks for graph neural networks (GNNs), specifically addressing the unique challenges posed by graphical data. Unlike traditional ...
MIT Technology Review

The next generation of neural networks could live in hardware

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...