babelphi

바벨파이

스터디 진행

• 바벨피쉬 페이스북 그룹 - https://www.facebook.com/groups/babelPish/
• 바벨피쉬 웹사이트 - http://babelpish.github.io/

교재

• PART I
  • (ML기초) 앤드류 응 ML 기초 - https://www.coursera.org/learn/machine-learning#syllabus
    • 강의슬라이드 - https://github.com/ShokuninSan/coursera-ml-class
    • 참고자료(발표노트 정리) - http://www.holehouse.org/mlclass/
  • (NLP기초) Introduction to Natural Language Processing
    • https://www.coursera.org/learn/natural-language-processing/
  • (DeepRL) Deep Reinfocement Learning
    • http://rll.berkeley.edu/deeprlcourse/

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스터디에서 진행할 보강&실습 자료는 링크합니다. 

커리큘럼

PART I (진행중)

• 2017년 2 ~

회차	일시	내용	발표자	발표자료
1	2/9	(NLP기초) Week One : Introduction 1/2	송치성
		(ML기초) 1. Introduction & Linear Regression with One Variable	김단비
		(DeepRL) Introduction and course overview	김무성
2	2/23	(NLP기초) Week Two: Introduction 2/2
		(NLP실습) python, docker 환경설정	김무성	도커환경설정, 파이썬3기초
		(ML기초) 2. Linear Regression with Multiple Variables
		(DeepRL) Supervised learning and decision making
3	3/9	(NLP기초) Week Three: NLP Tasks and Text Similarity (1)
		(NLP실습) nltk, konlpy, 은전한닢
		(ML기초) 3. Logistic Regression
		(DeepRL) Optimal control and planning
4	3/23	(NLP기초) Week Three: NLP Tasks and Text Similarity (2)
		(NLP실습)
		(ML기초) 4. Neural Networks: Representation
		(DeepRL) Review section: autodiff, backpropagation, optimization
5	4/6	(NLP기초) Week Four: Syntax and Parsing, Part 1 (1)
		(NLP실습)
		(ML기초) 5. Neural Networks: Learning
		(DeepRL) Learning dynamical system models from data
6	4/20	(NLP기초) Week Four: Syntax and Parsing, Part 1 (2)
		(NLP실습)
		(ML기초) 6. Advice for Applying Machine Learning
		(DeepRL) Learning policies by imitating optimal controllers
7	5/4	(NLP기초) Week Five: Syntax and Parsing, Part 2
		(NLP실습)
		(ML기초) 7. Machine Learning System Design
		(DeepRL) Guest lecture: Igor Mordatch, OpenAI