Stars
Serve, optimize and scale PyTorch models in production
High performance parallel reading of HDF5 files using PyTables, multiprocessing, and shared memory.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
A web GUI client of Project V which supports VMess, VLESS, SS, SSR, Trojan, Tuic and Juicity protocols. 🚀
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
Implemented the Kalman Filter Algorithms on GPU using CUDA programming language. Analysed the run-time performance gain obtained by parallel computation of the various stages of the algorithm.Speed…
fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing d…
A collection of loss functions for medical image segmentation
A python library for self-supervised learning on images.
The Few-Shot Bot: Prompt-Based Learning for Dialogue Systems
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
https://huyenchip.com/ml-interviews-book/
Code for the paper "Jukebox: A Generative Model for Music"
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Crypto Alerts, Watchlist & Portfolio Tracking App
Help you build your own crypto currency price alert system using [@Totoval](https://github.com/totoval/totoval) and [Pushover](https://pushover.net)
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
Interview prep materials sent me by FAANG companies. My LinkedIn post: https://bit.ly/3XWlwHX. Google Tech Dev Guide: https://techdevguide.withgoogle.com. Google Interview Prep: https://techdevguid…
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.
CRF-RNN PyTorch version http://crfasrnn.torr.vision