머신러닝 자료 링크들

각종 자료들이 많은 곳 : http://www.whydsp.org/284    

(모두의 연구소 출처, 특히 fundamental 한 이해를 도울 수 있는 영문 자료들) 

※ 기반 언어는 Python / OS는 Windows 10 (Docker)

텐서플로우 - Tensorflow

https://www.tensorflow.org/api_docs/python/

텐서플로 한글 문서 - Tensorflow kr

https://www.gitbook.com/book/tensorflowkorea/tensorflow-kr/details

텐서플로우 튜토리얼 깃허브

https://github.com/golbin/TensorFlow-Tutorials

파이토치 - Pytorch

http://pytorch.org/docs/master/

넘파이 - Numpy

https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.expand_dims.html

판다스 - Pandas

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.as_matrix.html

사이파이 - Scipy

http://www.scipy-lectures.org/packages/scikit-image/

Andrew Ng's Machine Learning 자료 정리되어 있는 곳

https://wikidocs.net/4289 (Andrew Ng)

Coding the Matrix 강의 Video

https://cs.brown.edu/video/channels/coding-matrix-fall-2014/?page=2

파이썬 Numpy + Linear Algebra (한국어)

https://www.slideshare.net/dahlmoon/numpy-20160519

Vector math (Khan academy, 한국어)

https://ko.khanacademy.org/computing/computer-programming/programming-natural-simulations/programming-vectors/a/intro-to-vectors

SNU 평생교육원 류근관 교수 통계학과 자료

http://snui.snu.ac.kr/ocw/index.php?mode=view&id=1494#class_room-tab

머신러닝 속 수학 (한국어)

https://mingrammer.com/translation-the-mathematics-of-machine-learning

Backpropagation을 python code로 이해하기 (English)

https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

CS231N syllabus    (CNN)

syllabus : http://cs231n.stanford.edu/syllabus.html

KOR : http://aikorea.org/cs231n/

youtube : https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk

CS244D                 (NLP)

syllabus : http://cs224d.stanford.edu/syllabus.html

youtube : https://www.youtube.com/watch?v=Qy0oEkCZkBI&list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG

CS224D pdf (spring 2016) : http://cs224d.stanford.edu/lectures/CS224d-Lecture15.pdf

information theory (한국어)

https://ratsgo.github.io/statistics/2017/09/22/information/

기계학습은 즐겁다 (한국어)

https://medium.com/@jongdae.lim/%EA%B8%B0%EA%B3%84-%ED%95%99%EC%8A%B5-machine-learning-%EC%9D%80-%EC%A6%90%EA%B2%81%EB%8B%A4-part-1-9a0297198ad8

seq2seq 모델 설명

https://tensorflowkorea.gitbooks.io/tensorflow-kr/content/g3doc/tutorials/seq2seq/

라온피플 머신러닝 (한국어) 블로그

http://laonple.blog.me/220623406512

텐서플로우 시작하기

https://www.tensorflow.org/get_started/mnist/pros

personal dataset 만들기

1) http://solarisailab.com/archives/1422

2) opencv : http://emaru.tistory.com/15

3) numpy와 scipy로 image processing : http://www.scipy-lectures.org/advanced/image_processing/

4) 이미지 다루기 : http://opencv-python.readthedocs.io/en/latest/doc/01.imageStart/imageStart.html

NLP with Python - 오렐리 책 free ver.

http://www.nltk.org/book/

뉴럴 네트워크

http://nmhkahn.github.io/NN

초보자를 위한 RNN과 LSTM 가이드

https://deeplearning4j.org/kr/lstm#초보자를-위한-rnns과-lstm-가이드