This project aims to enhance cybersecurity by using Convolutional Neural Networks (CNNs) to predict malware samples, improving the accuracy and efficiency of malware detection. The primary objective is to train a CNN model to accurately classify malware based on grayscale images, distinguishing between benign and malicious software

sciBASIC# is a kind of dialect language which is derive from the native VB.NET language, and written for the data scientist.

CNN projects beginner to master

Exercises on Machine Learning

使用OpenCV部署RecRecNet广角图像畸变矫正，包含C++和Python两个版本的程序

Fashion recommendation system that uses deep learning techniques to classify fashion items and recommend similar products

Biblioteca para manipulação de modelos de Redes Neurais

An attempt to build accurate models for detecting various brain diseases

Ejercicios practicos con redes neuronales con GPU Local

Keras CNN model for age prediction based of face photo

Deep learning approaches to PDEs

Convolutional Neural Network And Multimodal Learning with Graphic User Interface for Digital Pathology

Minerva project includes the minerva package that aids in the fitting and testing of neural network models. Includes pre and post-processing of land cover data. Designed for use with torchgeo datasets.

2D Max Pooling from NumPy

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.

2D Convolution from NumPy

This repository contains implementations of prominent computer vision deep learning architectures. The focus is on simplifying these architectures while relying solely on the PyTorch library. The goal is to provide accessible and streamlined versions of key models in the field.

3 different tasks, convolution and pooling implementations, CNNs and transfer learning.

vanilla, simple, node-oriented, compositive, optimized, frameworkn't{torchn't, TFn't, candlen't}

Classifying chest X-ray images into two categories: Pneumonia or Normal using a Convolutional Neural Network (CNN).