sujal-garg's Stars
AppFlowy-IO/AppFlowy
Bring projects, wikis, and teams together with AI. AppFlowy is the AI collaborative workspace where you achieve more without losing control of your data. The leading open source Notion alternative.
THUDM/ChatGLM2-6B
ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型
apitable/apitable
🚀🎉📚 APITable, an API-oriented low-code platform for building collaborative apps and better than all other Airtable open-source alternatives.
botpress/botpress
The open-source hub to build & deploy GPT/LLM Agents ⚡️
cvg/LightGlue
LightGlue: Local Feature Matching at Light Speed (ICCV 2023)
brainflow-dev/brainflow
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
SuperBruceJia/EEG-DL
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
CRED-CLUB/neopop-flutter
NeoPOP is CRED's inbuilt library for using NeoPOP components in your app
NYUMedML/GNN_for_EHR
Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"
dolongbien/HumanBehaviorBKU
Abnormal Human Behaviors Detection/ Road Accident Detection From Surveillance Videos/ Real-World Anomaly Detection in Surveillance Videos/ C3D Feature Extraction
builtree/simulate
A collection of simulations and visualizations for all sorts of stuff (Majorly Algorithmic or Mathematical)
grantgasser/Alzheimers-Prediction
An attempt to diagnose Alzheimer's disease earlier
imedslab/KNEEL
Hourglass Networks for Knee Anatomical Landmark Localization: PyTorch Implementation
Shaharyar07/E-Commerce
A repository containing a full-stack e-commerce application built with the MERN (MongoDB, Express.js, React.js, Node.js) stack. The project includes an admin dashboard for managing products, orders, and user data.
DARK-art108/Cotton-Leaf-Disease-Detection
🌿Classify Cotton Leaf Disease Images between Fresh or Diseased using Tensorflow Transfer Learning and Deploy it using a flask web server and Streamlit.🍂
KirbyDownB/GEICOChatBot
GEICO Hacktivates Hackathon 2019 1st Place Winner
SharmaPrateek196/CredCloneFlutter
zabir-nabil/autoocr
Python wrapper for cross platform tesseract OCR engine with multiple languages (e.g. Bangla)
bhavyaghai/Fluent
Fluent is an AI Augmented Writing Tool that assists People who Stutter write scripts which they can speak fluently
snovvcrash/daf-generator
Simple Delayed Auditory Feedback (DAF) generator. An anti-stuttering tool
anshulsingh8101/Cotton-Disease-Prediction-web_app-Transfer-learning
Using transfer learning to predict if there exists a cotton disease in the plant or not. The best were the inceptionv3 model and the ResNet50 model and then finally made a model for the web using flask for an end-to-end deployment of this project.
mohamed-said-ibrahem/MRNET-For-Knee-Diagnosis
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
dwvicy/PCOSmopolitan
PCOS Detection system using an accessible UX for everyone
shah-deep/Smart-Guidance
Web based AR application for Smart Educational Guidance
Theosau/Medical_Imaging
CNNs and ML regression methods for 3D brain MRI segmentation and patient age regression
auchinto-c/Cotton-Disease-Prediction
This project aims to classify images of cotton plant and cotton plant leaves into one of 4 categories.
gestory/flower
Game to help kids with amblyopia.
taharh/Google_Solution_Challenge_2021-Kallimni_bot
Our app’s goal is to introduce people to the world of psychological treatment, and familiarize the act of visiting a psychologist.
jeshu54/Machine-Learning---Heart-Disease-Predictor
Predicting heart disease using machine learning This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: ##Problem definition ##Data Evaluation, ##Features Modelling Experimentation, 1. Problem Definition: Given clinical parameters about a patient, can we predict whether or not they have heart disease? 2. Data: The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease\n", 3. Evaluation: If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project. 4. Features: This is where you'll get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset).
sadimanna/publication_codes
Codes for the algorithms proposed in the papers authored by Siladittya Manna