Biblioteca para manipulação de modelos de Redes Neurais

High-efficiency floating-point neural network inference operators for mobile, server, and Web

Convolutional Neural Network inference library running on CUDA

EBOP Model Automatic input Value Estimation Neural network

A Fortran-based feed-forward neural network library. Whilst this library currently has a focus on 3D convolutional neural networks (CNNs), it can handle most standard hidden layer forms of neural networks, with the plan to integrate more.

This toolkit is a curated collection of machine learning projects, resources, and utilities designed to assist both beginners and seasoned practitioners in their journey through the fascinating world of machine learning.

TNP WildVision project repository

This is a project focused on identifying the presence of pneumonia in chest X-ray images. Each image can be classified into one of three categories: Bacterial Pneumonia, Viral Pneumonia, or Normal.

Using CNN to identify tree leaves.

Machine learning pipeline trained to detect X-ray cavities in Chandra images of early-type galaxies.

Contextual Encoder-Decoder Network for Visual Saliency Prediction [Neural Networks 2020]

Techniques for deep learning with satellite & aerial imagery

Early prediction of liver cancer development using longitudinal MRI

learning materials for PyTorch beginners

🤖 GPU accelerated Neural networks in JavaScript for Browsers and Node.js

Official code repository of Laplacian Pyramid Pansharpening Network

This project examines various machine learning models for classifying text (restaurant and movie reviews) and images (CIFAR-10 dataset)

Official Implementation of ARACHNET: INTERPRETABLE SUB-ARACHNOID SPACE SEGMENTATION USING AN ADDITIVE CONVOLUTIONAL NEURAL NETWORK

Enhancing the resolution of human face images using CNN

This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based on accelerate/PyTorch, as well as scripts for experiments with CIFAR-10 and Tiny ImageNet.