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딥러닝 분야별 State-of-the-art (SOTA) 브라우저 출처: https://3months.tistory.com/493 [Deep Play]

• a_model_in_keras 설계
  https://frhyme.github.io/machine-learning/a_model_in_keras/

https://towardsdatascience.com/a-line-by-line-laymans-guide-to-linear-regression-using-tensorflow-3c0392aa9e1f

https://towardsdatascience.com/custom-tensorflow-loss-functions-for-advanced-machine-learning-f13cdd1d188a

• regression 한글 https://www.tensorflow.org/tutorials/keras/regression?hl=ko

• HOG descriptor mnist_compare
  https://github.com/dpetrini/mnist_compare

• AutoEncoder로 파생변수 만들기 -2 (모델링 파트) Catboost
  https://data-newbie.tistory.com/165

http://www.ciokorea.com/news/38148

• flow_from_dataframe 한글
  https://blog.naver.com/youseok0/221574784146
  https://blog.naver.com/yu_seon_kim/221612067393

https://github.com/fchollet/deep-learning-models/ https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening/discussion/30134

https://github.com/smart1004/doc/wiki/0-0-0-defect-defects-Surface-steel

• combining-the-outputs-of-multiple-models-into-one-model
  https://stackoverflow.com/questions/43150635/combining-the-outputs-of-multiple-models-into-one-model
  https://discuss.pytorch.org/t/concatenate-layer-output-with-additional-input-data/20462

• CNN_Layer_Talk
  https://tykimos.github.io/2017/01/27/CNN_Layer_Talk/

• 파이썬 – 이미지에서 색깔의 점을 카운트
  https://codeday.me/ko/qa/20190914/1453214.html

• python-opencv-color-spaces
  https://realpython.com/python-opencv-color-spaces/

https://docs.microsoft.com/ko-kr/azure/machine-learning/service/tutorial-auto-train-models

• guild.ai
  https://guild.ai/
  https://github.com/guildai/guildai

• opencv 교육
  http://www.kocw.net/home/search/kemView.do?kemId=434142

• 분류모델 (Classification)의 성능 평가
  https://bcho.tistory.com/tag/F1%20score
  F1 Score = 2PrecisionRecall/(Precision+Recall)

• 렉처노트 간 내용 복사
  https://www.clien.net/service/board/cm_andro/10360637

• bayesian-optimization
  http://research.sualab.com/introduction/practice/2019/02/19/bayesian-optimization-overview-1.html

• database-design-for-2d-matrix-algebra
  https://stackoverflow.com/questions/3193496/database-design-for-2d-matrix-algebra

• Overview of KNIME Software
  https://www.youtube.com/watch?v=oYHofs9ZHQ4

https://www.ibm.com/support/knowledgecenter/ko/SS3RA7_sub/modeler_crispdm_ddita/clementine/crisp_help/crisp_exploration_report_writing.html

https://datascience.stackexchange.com/questions/8847/feature-extraction-of-images-in-python
https://www.imageeprocessing.com/2017/10/read-image-using-skimage-module.html

• scikit-image
  https://scikit-image.org/docs/stable/auto_examples/

• Labeling 작업
  https://brunch.co.kr/@zigzag/13
  https://darkpgmr.tistory.com/16

• hyperparameter-optimization 툴들
  https://brendanhasz.github.io/2019/03/28/hyperparameter-optimization.html

https://github.com/chmp/ipytest

https://www.kaggle.com/huntermcgushion/bayesian-hyperparameter-optimization-and-xgboost https://github.com/HunterMcGushion/hyperparameter_hunter

• hyperlearn_Numba rewritten Sklearn
  https://github.com/danielhanchen/hyperlearn
  50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

• regression-error mse 등 평가지표
  https://partrita.github.io/posts/regression-error/

• steel_plate Surface
  https://github.com/smart1004/doc/wiki/0-0-0-defect-defects-Surface-steel

• Image-vision-DL
  https://github.com/smart1004/doc/wiki/0-0-Detect-Defects-Image-vision-DL

• xgboost
  https://github.com/smart1004/doc/wiki/000_xgboost_-python

• random-forest
  https://github.com/smart1004/doc/wiki/11_random-forest-python
  randomforestregressor gridsearchcv

• Automated Feature Engineering and Model Scoring using IBM Watson Studio Local
  https://github.com/IBM/model-mgmt-on-watson-studio-local

• matplotlib_chart
  https://github.com/smart1004/doc/wiki/0-0-0-matplotlib_chart

• phm
  https://github.com/smart1004/doc/wiki/0-0-0-phm

• 데이터프레임 고급 인덱싱
  https://datascienceschool.net/view-notebook/704731b41f794b8ea00768f5b0904512/

• LSTM-Long-Short-Term-Memory
  https://github.com/smart1004/doc/wiki/0-0-LSTM-Long-Short-Term-Memory

• regression
  https://github.com/smart1004/doc/wiki/0-0-nn_regression

• defect defects Surface with machine learning
  https://software.intel.com/en-us/articles/use-machine-learning-to-detect-defects-on-the-steel-surface
  https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-017-0197-y

http://faculty.neu.edu.cn/yunhyan/NEU_surface_defect_database.html

https://github.com/rplawate/steel_plate

• anomaly_detection
  http://intothedata.com/02.scholar_category/anomaly_detection/

http://alb-was-1161251887.ap-northeast-2.elb.amazonaws.com:9090/M00/M00S22/login.mvc

• Robotic-Process-Automation
  https://github.com/smart1004/doc/wiki/0-0-Robotic-Process-Automation

https://github.com/smart1004/doc/wiki/0-0-sds-ai

h2o spark
꿈틀대는 머신러닝 시장, 한국은 어디쯤 왔나
https://www.boannews.com/media/view.asp?idx=67442

• ISV Showcase: End-to-end Machine Learning using H2O on Azure : Build 2018
  https://blog.naver.com/kusung25/221489707427

• H2O_Feature-Engineering
  https://github.com/smart1004/doc/wiki/0-0-H2O_Feature-Engineering

• ml-sparklyr
  https://github.com/smart1004/doc/wiki/0-0--ml-sparklyr

• mingw--gcc
  https://github.com/smart1004/doc/wiki/0-0-a-mingw--gcc

• NSML Documentation
  https://n-clair.github.io/vision-docs/_build/html/ko_KR/index.html

https://github.com/ml1004/edu/wiki

• parameter-tuning https://github.com/smart1004/doc/wiki/0-0-parameter-tuning

• tensorflow_install https://github.com/smart1004/learn_src/wiki/tensorflow_install

matplotlib
https://medium.com/@peteryun/python-matplotlib-%EA%B8%B0%EB%B3%B8-6e23e5fd2f16

모두를 위한 머신러닝/딥러닝 강의
https://hunkim.github.io/ml/

https://www.kaggle.com/

Best 참고 소스코드 위치

lstm, 텐스플로우
https://github.com/smart1004/doc1/wiki/Best-%EC%B0%B8%EA%B3%A0-%EC%86%8C%EC%8A%A4%EC%BD%94%EB%93%9C-%EC%9C%84%EC%B9%98

ㅁ jupyter-notebook
https://github.com/smart1004/doc/wiki/jupyter-notebook-%EB%8B%A8%EC%B6%95%ED%82%A4

https://github.com/smart1004/doc/wiki/konlpy-customized_konlpy

machine learning in action pdf 머신 러닝 인 액션
머신 러닝 an algorithimic perspective

Big Java

tf-idf @@@@ https://blog.naver.com/happyrachy/221285427229

딥러닝-접근법
https://github.com/smart1004/doc/wiki/%EB%94%A5%EB%9F%AC%EB%8B%9D-%EC%A0%91%EA%B7%BC%EB%B2%95

중앙대 김중헌 교수 https://blog.naver.com/hidoooo blog.naver.com/global783

pip intall konlpy

install-tensorflow

https://github.com/smart1004/learn_src/wiki/tensorflow_install

한국통신학회 kics

파이썬-머신러닝
https://tensorflow.blog/%ED%8C%8C%EC%9D%B4%EC%8D%AC-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D/

이미지뷰어 https://sourceforge.net/projects/sagethumbs/ http://www.nyam.pe.kr/dev/imagine/ 열라 느림