Haejoong Lee's repositories
gym-homeenergy
Reinforcement learning based home energy saving project
Federated-Learning
연합학습을 공부하는 자료입니다.
Research-Review
딥러닝 논문을 리뷰하고 리뷰한 자료를 만든 공간입니다.
segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
torch_study
스터디 자료 저장소
Electric-vehicle-battery-life-prediction-study
Summary of thesis for electric vehicle battery life prediction
applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
awesome-nilm
A curated resources of awesome NILM resources
book-dataset
This dataset contains 207,572 books from the Amazon.com, Inc. marketplace.
Deep-Reinforcement-Learning-with-Python
Deep Reinforcement Learning with Python, Second Edition, published by Packt
EnergyProject
Reinforcement learning based home energy saving project
fsdl-text-recognizer-project
The source repository is at https://github.com/full-stack-deep-learning/fsdl-text-recognizer
GNNPapers
Must-read papers on graph neural networks (GNN)
HyperNetworks
PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet (Residual Networks)
minimalRL
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
Optimazation
Repository for optimization classes.
Paper_Reading_List
Recommended Papers. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Learning (cs.LG)
pattern_classification
패턴인식 연습문제 및 code 정리
PRML
PRML algorithms implemented in Python
psa
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018
python-for-coding-test
[한빛미디어] "이것이 취업을 위한 코딩 테스트다 with 파이썬" 전체 소스코드 저장소입니다.
python_algorithm_interview
DeepHaeJoong/python_algorithm_interview
reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
Semantic-Segmentation
Semantic Segmentation
seq2point-nilm
Sequence-to-point learning for non-intrusive load monitoring
shap
A game theoretic approach to explain the output of any machine learning model.