Model performance and tuning analysis conducted on the CIFAR10 and CIFAR100 datasets. Convolutional Neural Network (CNN), Gated Multilayer Perceptron (gMLP), and Vision Transformer (ViT) model architectures are utilized. The study is built using PyTorch, PyTorch Lightning for clean and concise code and Optuna for hyperparameter tuning.

This project aims to develop an image classification system to identify dog breeds using deep learning models. The classifier leverages pre-trained models from the PyTorch library, including ResNet18, AlexNet, and VGG16, to achieve accurate breed identification from images.

Training MLP and CNN on MNIST dataset

Image Content Filtering Using CNN and Transfer Learning

Your health companion on Android. Detect diseases via X-rays/MRIs, store reports securely, access educational content, join a supportive community, and chat for health info.

Predicting mutation effects based on deep mutational scanning data

This repository contains a deep learning project for classifying images of cats and dogs. The project includes a custom Convolutional Neural Network (CNN) and a fine-tuned ResNet50 model, both trained on a dataset imported from Kaggle.

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

This project is a comprehensive collection of machine learning algorithms implemented from scratch, accompanied by detailed documentation on the underlying mathematics.

Tomato leaves: a Vision and Cognitive Systems (VCS) Project | a.a.2023-204

Using CNN for image classification

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.

Deep learning model that predicts the phase of the moon 🌒🌓🌔🌕🌖🌗🌘

Hardware Acceleration For 2D Convolutions For Neural Networks

A deep learning project aimed at early detection of breast cancer by classifying tumors as benign or malignant based on features extracted from cell images. The project demonstrates data preprocessing, model training, and evaluation using various deep learning algorithms to achieve high accuracy in predictions.

Template implementation of a ConvNet for object recognition

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

Exercises on Machine Learning

Canada Wildfire Prediction Using Deep Learning