HyeongD

Algorithms-Stanford.University

Algorithms - Learn To Think Like A Computer Scientist - Coursera

darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

EffectiveTensorflow

TensorFlow tutorials and best practices.

ExchangeSharp

ExchangeSharp is a powerful, fast and easy to use .NET/C# API for interfacing with many crypto currency exchanges. REST and web sockets are supported.

LeapUnrealModules

Leap Motion Unreal modules and example content.

mchat

Create chat program based on meteor

NBitcoin

Comprehensive Bitcoin library for the .NET framework.

NewProject

pumpkin-book

《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book

quiknode_explorer

Build a Blockchain Explorer with Quiknode.io | Ethereum dApp Tutorial

HyeongD/catalyst

Accelerated deep learning R&D

HyeongD/chatgpt-retrieval-plugin

The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.

HyeongD/DeepLabCut

Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

HyeongD/detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

HyeongD/dqn_zoo

DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.

HyeongD/gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

HyeongD/GPTs-are-GPTs

HyeongD/graphcore-examples

Example code and applications for machine learning on Graphcore IPUs

HyeongD/gym

A toolkit for developing and comparing reinforcement learning algorithms.

HyeongD/homework_fall2023

HyeongD/HyeongD.github.io

HyeongD/jukebox

Code for the paper "Jukebox: A Generative Model for Music"

HyeongD/Kaggle

Some activities

HyeongD/mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

HyeongD/notebooks

Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

HyeongD/openai-dotnet

The official .NET library for the OpenAI API

HyeongD/opencv_zoo

Model Zoo For OpenCV DNN and Benchmarks.

HyeongD/optimum-graphcore

Blazing fast training of 🤗 Transformers on Graphcore IPUs

HyeongD/pytorch-3dunet

3D U-Net model for volumetric semantic segmentation written in pytorch

HyeongD/PyTorch-Quantization-Aware-Training

PyTorch Quantization Aware Training Example

HyeongD/ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

HyeongD/stable-baselines3

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

HyeongD/tfjs-models

Pretrained models for TensorFlow.js

HyeongD/ultralytics

NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

HyeongD/unilidar_sdk

SDK for Unitree L1 LiDAR

HyeongD/Video-Pre-Training

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

HyeongD/yolo_tracking

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

HyeongD/yolov10

YOLOv10: Real-Time End-to-End Object Detection

HyeongD/yolov9

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

HyeongD/yolov9-bytetrack-tensorrt

Integration of YOLOv9 with ByteTracker