3D Neural Denoising for Track Reconstruction and Pattern ID @ LHCb TORCH Detector

TensorFlow 101: Introduction to Deep Learning

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

Deep Convolutional Neural Networks and Machine Learning Models for Analyzing Stellar and Exoplanetary Telescope Spectra

Implementation from scratch (using numpy arrays) of a framework based on keras interface which allows to build and train Fully Connected Networks and Convolutional Neural Networks (CNNs).

moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.

Projects created for the Spring 2023 Neural Networks Course at the University of Missouri - Columbia

Artificial Neural Network from scratch using Javascript on the browser

Content-Based Image Retrieval Using CNN and Hash

Keras-Tensorflow implementation of complex-valued convolutional neural networks

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.

Open source Python library for building bioimage analysis pipelines

PotatoTech is an Android application designed to classify potato leaf diseases. Using advanced machine learning models, the app accurately identifies whether a potato leaf is healthy or diseased, specifically detecting Early Blight and Late Blight.

Detection and Classification of tomato diseases for farmers in Nigeria using cutting-edge convolution neural networks (CNN) and leveraging the power of VGG19 model as a transfer model.

MIST: A simple, scalable, and end-to-end framework for 3D medical imaging segmentation.

This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes.

Classifying Lung Cancer using a combination of Convolutional Neural Network as features extractor and Random Forest as Classifier

GreenGuard is a cutting-edge picture classification initiative that aims to transform agriculture by giving farmers an automated way to identify plant illnesses early on. In order to reduce crop losses and enhance farming methods, GreenGuard uses a convolutional neural network to detect and categorize sick plants.

DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling