An efficient neural screening approach rapidly identifies circuit modules governing distinct behavioral transitions in response to pathogen exposure.

Abstract: Spiking neural networks (SNNs) are attractive algorithms that pose numerous potential advantages over traditional neural networks. One primary benefit of SNNs is that they may be run ...

Abstract: This study introduces a novel strategy for waste segregation employing Convolutional Neural Networks (CNNs) and Python programming. By harnessing CNNs’ image feature extraction capabilities, ...

The UN Charter Libraries Knowledge Network represents a forward-looking initiative to strategically reposition and expand the United Nations Depository Library Programme. This effort seeks to ...

As a result, the on-chip learning-based neuromorphic system achieved up to 20,000 times faster processing speed while maintaining similar interpretation accuracy to existing conventional techniques.

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland ...

This repository contains my implementation of a feed-forward neural network classifier in Python and Keras, trained on the Fashion-MNIST dataset. It closely follows the tutorial by The Clever ...

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 ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. This study introduces PROFIS, a new generative model capable of the design of ...