Pinned Repositories
agents
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
ai4code-baseline
Early solution for Google AI4Code competition
AI4Code-bronze
camera-calibration
Camera Calibration with Checkerboard
cs285_homework
Berkeley CS285 (Deep RL) 2023 Fall Homework
loftr-pytorch
Reimplementation of LoFTR model and training in PyTorch, end-to-end transformer model for image matching
machine_learning_fundamentals
To study and understand machine learning fundamental algorithms.
simple-stereo-matching
Implement simple stereo matching algorithms in fully Pytorch.
unet-torch-mps
Implement Unet and its variants from scratch using PyTorch. Train those models and compare the results.
HJoonKwon's Repositories
HJoonKwon/machine_learning_fundamentals
To study and understand machine learning fundamental algorithms.
HJoonKwon/loftr-pytorch
Reimplementation of LoFTR model and training in PyTorch, end-to-end transformer model for image matching
HJoonKwon/ai4code-baseline
Early solution for Google AI4Code competition
HJoonKwon/AI4Code-bronze
HJoonKwon/camera-calibration
Camera Calibration with Checkerboard
HJoonKwon/coursera-deep-learning-specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
HJoonKwon/cs148
Introduction to computer graphics (CS148) provided by Stanford.
HJoonKwon/cs285_homework
Berkeley CS285 (Deep RL) 2023 Fall Homework
HJoonKwon/simple-stereo-matching
Implement simple stereo matching algorithms in fully Pytorch.
HJoonKwon/unet-torch-mps
Implement Unet and its variants from scratch using PyTorch. Train those models and compare the results.
HJoonKwon/cs229-2018-autumn
All notes and materials for the CS229: Machine Learning course by Stanford University
HJoonKwon/deep_learning_fundamentals
Code implementation of computer vision models for practice based on pytorch and einops.
HJoonKwon/detectron2
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
HJoonKwon/HJoonKwon.github.io
HJoonKwon/kaggle-guie-practice
HJoonKwon/kornia
Open Source Differentiable Computer Vision Library
HJoonKwon/machine-learning-engineering-for-production
Machine learning engineering for production provided by Deeplearning.ai
HJoonKwon/makemore
self-study notes for Andrej Karpathy's makemore series
HJoonKwon/micrograd
Self-study notes for Andrej Karpathy's micrograd lecture
HJoonKwon/ml-aspanformer
HJoonKwon/ml-cvnets
CVNets: A library for training computer vision networks
HJoonKwon/mmclassification
OpenMMLab Image Classification Toolbox and Benchmark
HJoonKwon/mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
HJoonKwon/nanoGPT-lecture
Study notes for Andrej Karpathy's nanoGPT lecture
HJoonKwon/object-oriented-design
Making toy games for practice of OOP
HJoonKwon/PythonRobotics
Python sample codes for robotics algorithms.
HJoonKwon/segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
HJoonKwon/SuperGluePretrainedNetwork
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
HJoonKwon/udacity-flyingcar
HJoonKwon/yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite