2016 YONSEI BIG DATA CONFERENCE에서 발표된 팀 프로젝트입니다. 연세대학교의 마일리지 수강신청 데이터를 이용해 학과 간 차별이 존재하는지를 밝히고, 전공자 보호 강화가 해결책이 될 수 있는지 살펴보았습니다.
5기 조용래, 8기 이한별, 이수진
연세대학교 빅데이터 학술동아리 YBigTa
Facebook : facebook.com/yonseibigdata
2016 YONSEI BIG DATA CONFERENCE에서 발표된 팀 프로젝트입니다. 연세대학교의 마일리지 수강신청 데이터를 이용해 학과 간 차별이 존재하는지를 밝히고, 전공자 보호 강화가 해결책이 될 수 있는지 살펴보았습니다.
5기 조용래, 8기 이한별, 이수진
연세대학교 빅데이터 학술동아리 YBigTa
Facebook : facebook.com/yonseibigdata
GDG Campus Korea에서 개최한 'Daily 만년 Junior들의 이야기 : 델리만주' 밋업에서 발표했던 내용으로 대학원 석사 입학 후부터 오늘날까지 어떤 활동들을 했는지 정리했습니다. 대학원생 분들과 게임 프로그래머 취업을 준비하시는 분들께 많은 도움이 되었으면 합니다.
100% Serverless big data scale production Deep Learning Systemhoondong kim
- BigData Sale Deep Learning Training System (with GPU Docker PaaS on Azure Batch AI)
- Deep Learning Serving Layer (with Auto Scale Out Mode on Web App for Linux Docker)
- BigDL, Keras, Tensorlfow, Horovod, TensorflowOnAzure
- E-commerce BigData Scale AI Journey
- BigData Scale Deep Learning Production System Use Case
- Deep Learning, Cloud PaaS, Microservices, DevOps, etc.
- E-Commerce AI Production System Strategy
Second week, what is an Artivicial Inteligence?.pdfssuser5a82521
Slide 1: Title Slide
Title: "Understanding Artificial Intelligence (AI)"
Subtitle: "An Introduction to the World of Intelligent Machines"
Image: Illustration depicting futuristic technology or AI-related imagery.
Slide 2: Introduction to AI
Definition of AI: "Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and mimic human actions."
Brief history: Highlight key milestones in AI development, from early beginnings to modern advancements.
Slide 3: Types of AI
Narrow AI: Explanation and examples of AI designed for specific tasks, such as virtual assistants, recommendation systems, and self-driving cars.
General AI: Overview of the concept of AGI (Artificial General Intelligence), which aims to mimic human intelligence across a broad range of tasks.
Slide 4: How AI Works
Algorithms: Explanation of how AI systems use algorithms to process data, learn from it, and make decisions or predictions.
Data: Importance of high-quality data for training AI models.
Training: Overview of the training process, including supervised, unsupervised, and reinforcement learning.
Slide 5: Applications of AI
Industry: Examples of AI applications in various industries, such as healthcare (diagnosis assistance), finance (fraud detection), and retail (personalized recommendations).
Everyday life: Highlight how AI impacts daily life, including social media algorithms, virtual assistants, and smart home devices.
Slide 6: Ethical Considerations
Bias: Discussion on the potential for AI systems to inherit biases from their training data and the importance of addressing this issue.
Privacy: Considerations regarding the collection and use of personal data by AI systems, and the need for transparent data practices.
Job displacement: Exploration of the potential impact of AI on employment and the importance of retraining and reskilling the workforce.
Slide 7: Future of AI
Advancements: Speculation on future advancements in AI technology, including the potential for AGI and the ethical implications.
Challenges: Highlighting ongoing challenges in AI research, such as ensuring safety, fairness, and accountability.
Opportunities: Discussion on the potential benefits of AI for society, including improved healthcare, increased productivity, and enhanced decision-making.
Slide 8: Conclusion
Recap: Summarize key points covered in the presentation, emphasizing the significance of AI in today's world.
Call to action: Encourage further exploration of AI-related topics and participation in discussions about its future impact.
Slide 9: Q&A
Open the floor for questions and discussion, allowing the audience to clarify any doubts or share their thoughts on AI.
Slide 10: Thank You
Express appreciation to the audience for their attention and participation in the presentation.
꿈꾸는 데이터 디자이너 시즌2 교육설명회 슬라이드 입니다. 시즌2에 대한 정보와 시즌1에서의 결과에 대한 설명입니다.
www.facebook.com/datadesigner2015
https://www.facebook.com/groups/datadesigner/
www.datadesigner.org
GDG Campus Korea에서 개최한 'Daily 만년 Junior들의 이야기 : 델리만주' 밋업에서 발표했던 내용으로 대학원 석사 입학 후부터 오늘날까지 어떤 활동들을 했는지 정리했습니다. 대학원생 분들과 게임 프로그래머 취업을 준비하시는 분들께 많은 도움이 되었으면 합니다.
100% Serverless big data scale production Deep Learning Systemhoondong kim
- BigData Sale Deep Learning Training System (with GPU Docker PaaS on Azure Batch AI)
- Deep Learning Serving Layer (with Auto Scale Out Mode on Web App for Linux Docker)
- BigDL, Keras, Tensorlfow, Horovod, TensorflowOnAzure
- E-commerce BigData Scale AI Journey
- BigData Scale Deep Learning Production System Use Case
- Deep Learning, Cloud PaaS, Microservices, DevOps, etc.
- E-Commerce AI Production System Strategy
Second week, what is an Artivicial Inteligence?.pdfssuser5a82521
Slide 1: Title Slide
Title: "Understanding Artificial Intelligence (AI)"
Subtitle: "An Introduction to the World of Intelligent Machines"
Image: Illustration depicting futuristic technology or AI-related imagery.
Slide 2: Introduction to AI
Definition of AI: "Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and mimic human actions."
Brief history: Highlight key milestones in AI development, from early beginnings to modern advancements.
Slide 3: Types of AI
Narrow AI: Explanation and examples of AI designed for specific tasks, such as virtual assistants, recommendation systems, and self-driving cars.
General AI: Overview of the concept of AGI (Artificial General Intelligence), which aims to mimic human intelligence across a broad range of tasks.
Slide 4: How AI Works
Algorithms: Explanation of how AI systems use algorithms to process data, learn from it, and make decisions or predictions.
Data: Importance of high-quality data for training AI models.
Training: Overview of the training process, including supervised, unsupervised, and reinforcement learning.
Slide 5: Applications of AI
Industry: Examples of AI applications in various industries, such as healthcare (diagnosis assistance), finance (fraud detection), and retail (personalized recommendations).
Everyday life: Highlight how AI impacts daily life, including social media algorithms, virtual assistants, and smart home devices.
Slide 6: Ethical Considerations
Bias: Discussion on the potential for AI systems to inherit biases from their training data and the importance of addressing this issue.
Privacy: Considerations regarding the collection and use of personal data by AI systems, and the need for transparent data practices.
Job displacement: Exploration of the potential impact of AI on employment and the importance of retraining and reskilling the workforce.
Slide 7: Future of AI
Advancements: Speculation on future advancements in AI technology, including the potential for AGI and the ethical implications.
Challenges: Highlighting ongoing challenges in AI research, such as ensuring safety, fairness, and accountability.
Opportunities: Discussion on the potential benefits of AI for society, including improved healthcare, increased productivity, and enhanced decision-making.
Slide 8: Conclusion
Recap: Summarize key points covered in the presentation, emphasizing the significance of AI in today's world.
Call to action: Encourage further exploration of AI-related topics and participation in discussions about its future impact.
Slide 9: Q&A
Open the floor for questions and discussion, allowing the audience to clarify any doubts or share their thoughts on AI.
Slide 10: Thank You
Express appreciation to the audience for their attention and participation in the presentation.
꿈꾸는 데이터 디자이너 시즌2 교육설명회 슬라이드 입니다. 시즌2에 대한 정보와 시즌1에서의 결과에 대한 설명입니다.
www.facebook.com/datadesigner2015
https://www.facebook.com/groups/datadesigner/
www.datadesigner.org
9. Python + MySQL
!
!
!
• 배우기 쉬운 프로그래밍 언어
• 다양한 모듈
• www.python.org
• Python(Wikipedia)
!
!
!
• 가장 많이 사용되는 관계형
데이터베이스
• www.mysql.com
• MySQL(Wikipedia)
14. 공부할 내용
• 파이썬을 사용한 웹 데이터 수집 및 전처리
• 온라인 커뮤니티 게시글, 네이버 뉴스 검색 API, 트위터
• MySQL 스키마, 자료형
• SQL 문법
15. Keywords
• Python
• MySQL
• SQL
• RDBMS
• JSON
• XML
• HTML Parsing
• BeautifulSoup
• CSS Selector
• AWS
• Crontab
• RSS
• API
• SSH
16. 스터디를 시작하기 전에…
• 노트북 준비
• Eclipse 설치
• MySQL Workbench 설치
• Putty 다운로드(윈도우 사용자)
• AWS(http://aws.amazon.com/) 계정 만들기
• Codecademy(http://www.codecademy.com/)에서
파이썬 공부 시작
19. • 대상 : Machine Learning을 처음 배워 보시는
분과 응용을 해보고 싶으신 분
• 계획 : Coursera Stanford Machine Learning
강의 수강 후 kaggle의 데이터에등 다양한 데이
터에 적용
• 모임 : X, 과제 체크, 적용 부터는 모임 있슴다!
Machine Learning%
20. 월 화 수 목 금 토 일
1 2 3 4 5 6
7 8 9 10 11 12 13
14 15 16 17 18 19 20 인강
끝
21데이터
적용
22 23 24 25 26 27
28 29 30 31
Machine Learning%
21. R 스터디 LEVEL 1_week 00
연세대학교 빅데이터 동아리 YBigTa
R Study
3기 구지연
LEVEL 1
22. R 스터디 LEVEL 1_week 00
Thank You
감사합니다
연세대학교 빅데이터 동아리 YBigTa
23. R 스터디 LEVEL 1_week 00
Study 소개
대상 : R을 처음 다뤄 보시는분,
R을 다뤄보긴 했지만 다시 기초를 닦고
싶으신분
요일 : 아마 일요일
진행 내용 : R 기초문법 + 약간의 응용(R 맛보기)
+ 과제(열심히 하실분 오세요^^)
24.
25. 수강대상 – R초급 내용은 시시하다! 난 좀 더 심화 된 것을 배우고
싶다고 생각하는 사람.
지난 학기의 R 중급 스터디와 동일한 내용을 다룰 예정.
수강요일 – 스터디원이 확정 된 후 협의.
진행내용 – 다양한 차트, 회귀모델 만들기, 상관분석, 시계열 분석,
텍스트 마이닝 등.
R 중급 스터디 계획