Be the only one, not the best one

[온라인 무료 강의] R로 하는 텍스트 전처리( 박찬엽 SK텔레콤 / T아카데미)

학습내용
1. 단정한 데이터란 무엇인지, 텍스트 데이터에서는 어떻게 접목되는지 이해한다.
2. 한글 데이터 분석에 필요한 Rmecabko / KoLNP 사용법을 알아보고, 한글 데이터 전처리 방법을 알아본다.

<학습대상>
R 프로그래밍이 가능하며, stringr 패키지와 정규표현식에 대하 이해가 있으신 분

<강의목록>
[1강] Tidyverse I - 파이프연산자(%/%), dplyr
[2강] Tidyverse II - tidy data, tidy text
[3강] 형태소분석 패키지 설치 - KoNLP, RmecanKo
[4강] 형태소분석 패키지 사용실습 - Token화, 불용어 제거, 정규표현식
[5강] 정량 지표 I - 단순출현빈도, 동시출현빈도
[6강] 정량 지표 II - tf-idf, 감성분석

* 박찬엽 선생님 github : https://mrchypark.github.io/
* 출처 : https://tacademy.skplanet.com/live/player/onlineLectureDetail.action?seq=166
https://www.facebook.com/113979985329905/posts/2664110800316798/?sfnsn=mo

Generate photorealistic facial images under new viewpoints or illumination conditions using this!

https://www.facebook.com/groups/DeepNetGroup/permalink/1018236828569199/?sfnsn=mo

스텐포드 딥러닝 수업이 정말 많네요. 이번학기 새롭게 업데이트된 자료와 코스도 많으니 추운날 방에서  보고 있으면 이번 겨울이 빠르게 지날것 같습니다. 모두 딥러닝/AI와 함께 따뜻한 겨울 되기실!

Deep Learning

[http://web.stanford.edu/class/cs230/](http://web.stanford.edu/class/cs230/)

[ Natural Language Processing ]

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)

[http://web.stanford.edu/class/cs124/](http://web.stanford.edu/class/cs124/)

CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284)

[http://web.stanford.edu/class/cs224n/](http://web.stanford.edu/class/cs224n/)

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)

[http://web.stanford.edu/class/cs224u/](http://web.stanford.edu/class/cs224u/)

CS 276: Information Retrieval and Web Search (LINGUIST 286)

[http://web.stanford.edu/class/cs](http://web.stanford.edu/class/cs224u/)276

[ Computer Vision ]
CS 131: Computer Vision: Foundations and Applications

http://[cs131.stanford.edu](http://cs131.stanford.edu/)

CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning

[http://web.stanford.edu/class/cs205l/](http://web.stanford.edu/class/cs205l/)

CS 231N: Convolutional Neural Networks for Visual Recognition

[http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)

CS 348K: Visual Computing Systems

[http://graphics.stanford.edu/courses/cs348v-18-winter/](http://graphics.stanford.edu/courses/cs348v-18-winter/)

[ Others ]

CS224W: Machine Learning with Graphs([Yong Dam Kim](https://www.facebook.com/yongdam.kim) )

[http://web.stanford.edu/class/cs224w/](http://web.stanford.edu/class/cs224w/)

 
CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)

[https://canvas.stanford.edu/courses/51037](https://canvas.stanford.edu/courses/51037)

CS 236: Deep Generative Models

[https://deepgenerativemodels.github.io/](https://deepgenerativemodels.github.io/)

CS 228: Probabilistic Graphical Models: Principles and Techniques

[https://cs228.stanford.edu/](https://cs228.stanford.edu/)

CS 337: Al-Assisted Care (MED 277)

[http://cs337.stanford.edu/](http://cs337.stanford.edu/)

CS 229: Machine Learning (STATS 229)

[http://cs229.stanford.edu/](http://cs229.stanford.edu/)

CS 229A: Applied Machine Learning

[https://cs229a.stanford.edu](https://cs229a.stanford.edu/)

CS 234: Reinforcement Learning

http://[s234.stanford.edu](http://cs234.stanford.edu/)

CS 221: Artificial Intelligence: Principles and Techniques

[https://stanford-cs221.github.io/autumn2019/](https://stanford-cs221.github.io/autumn2019/)
https://m.facebook.com/groups/255834461424286?view=permalink&id=1051374671870257&sfnsn=mo