Popular GitHub Repositories for Heart Growth

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

Resources on the topic of digital morphogenesis (creating form with code). Includes links to major articles, code repos, creative projects, books, software, and more.

Speech to Text to Speech. Song now playing. Sends text as OSC messages to VRChat to display on avatar. (STTTS) (Speech to TTS) (VRC STT System) (VTuber TTS)

a generative algorithm

It is a medical chatbot that will provide quick answers to FAQs by setting up rule-based keyword chatbots.

Remote heart rate detection through Eulerian magnification of face videos

ECG classification programs based on ML/DL methods

open-source browser extension that leverages the power of the AI to generate engaging replies for social media growth.

A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

?? illufly 是自我进化的 Agent 框架: 基于自我进化，快速创造价值

Using the SWELL dataset from Kaggle, we've built 2 machine learning models to predict whether or not a person is under stress using Heart Rate Variability(HRV) which can be collected from modern wearables such as fitbit devices and apple watches.

Code and data for "Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation" (EMNLP 2023)

End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation. A replication of the RhythmNet model.

Scrape Reddit for marketing pain points using GPT — built to fuel Cronlytic's growth.

Intelligent Driver Monitoring system for Autonomous Vehicles

Stress classifier with AutoML

This app leverages generative AI to intelligently manage your sales inbox and CRM, auto-draft replies using your sales playbook & company branding, and more.

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease