Rubbing & Scrubbing My Data

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville.  Deep learning . MIT Press, 2016. [ Link ] 1. Introduction [ Sum-up ] * AI는 컴퓨터가 잘하는 일(formal task: e.g, 수치계산, 규칙작업)말고 사람이 (직감적으로) 잘하는 일(informal task: e.g., 언어/이미지 이해)을 해결하는데 목표를 가진다.  * 사실 정작 사람은 직감적으로 잘하는 일들(informal tasks)을 왜 잘하는지 모른다. 우리는 그것들을 세상/지식으로부터 배우는 데, 그것들은 지극히 주관적(subjective)이고 직감적(intuitive)이기 때문에 형태적으로 표현(formally describe)하기 힘들다. * AI (deep learning)는 세상/문제를 '계층적 개념'(hierarchy of concepts) 형태로 이해해서 사람이 어떤 개념/형태를 명시(formally specify)해주지 않아도 사람이 잘하는 직감적인 문제(complicated concepts, informal tasks, informal knowledge)들을 자체 지능적으로 해결하고자 한다. * AI에서 Knowledge base (hard-code knowledge)는 사람이 knowledge를 직접 design해줘야 한다. 따라서, (단순 규칙을 통한 추론만으로는) 복잡한 세상/문제의 패턴을 인식하기가 어렵다. 반면, Machine Learning은 사람이 아닌 (수많은) raw data로부터 AI만의 knowledge를 얻는 방식이다. data로부터 직접 얻기 때문에 'reliability'가 있다. 물론 양에 비례할 것이다. * Machine Learning의 문제는 representation/feature에 '상당히' 의존한다는 것이다. 다시 말해, feature를 잘 design하기 위해서는 domain expert로부터의 h...

University of Science and Technology Electronics and Telecommunications Research Institute (ETRI) Vision System Research Team, Jae-Young LEE March 1, 2016 ~ June 24, 2016 https://sites.google.com/site/roricljy/ 1. Geometry: Vectors    - Vector & Scalar & Vector Equality, Addition, and Subtraction    - Euclidean Vector    - Trigonometry Review    - Polar Representation    - Dot product & Cross product    - Homework #1    - Homework #2 2. Geometry: 공간 도형    - 도형    - 직선의 방정식    - 평면의 방정식    - 부등식의 영역    - 도형의 방정식과 함수    - Homework #3 3. 행렬 연산    - Introduction    - Matrices and matrix algebra    - Matrices and systems of linear equations 4. 벡터공간, 선형시스템    - Vector Spaces    - Basis and Dimension    - Rank of a Matrix and Systems of Linear Equations    - 기타 선형대수학에서 알아두어야 할 것들 ...

by Kristin Sainani CONTENTS 1. Descriptive statistics and looking at data 2. Review of study designs; Measures of disease risk and association 3. Probability, Bayes' Rule, Diagnostic Testing 4. Probability distributions 5. Statistical Inference 6. P-values (errors, statistical power, and pitfalls) 7. Statistical Tests 8. Regression Analysis 9. Logistic Regression, Cox Regression 1. Descriptive statistics and looking at data 1.1 Types of Data 1.1.1 Quantitative Variable It is a numerical data(e.g., Age, Blood pressure, BMI, Pulse) that you can add, subtract, multiply, and divide. ㆍ Continuous (quantitative) variable: can theoretically take on any value within a given range (e.g., height=68.99955... inches) ㆍ Discrete (quantitative) variable: can only take on certain values (e.g., count data) However, In the real world, sometimes the distinction between continuous and discrete actually doesn't make much difference. For example, when we analyze a family size from discrete value(e...

By Andrew Ng 1. Logistic Regression      1.1   Visualizing Data Part             └ plotData function     1.2   Advanced  Optimization  Part             └ mapFeature function             └ costFunctionReg function             └ fminunc function         1.3   Decision Boundary and Prediction   Part             └ plotDecisionBoundary function             └ predict function 1. Logistic Regression We'll implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly. Suppose you're the product manager of factory and you have the test results for some microchips on two different tests. From...

by ChengXiang Zhai CONTENT 1. Overview Text Mining and Analysis 2. Natural Language Processing & Text Representation 3. Word Association Mining and Analysis      └ Paradigmatic      └ Syntagmatic 7. Topic Mining and Analysis 8. Probabilistic Topic Models 9. Probabilistic Latent Semantic Analysis (PLSA) 10. Latent Dirichlet Allocation (LDA) 11. Text Clustering 12. Text Categorization 13. Opinion Mining and Sentiment Analysis 14. Latent Aspect Rating Analysis 15. Text-Based Prediction 16. Contextual Text Mining 3. Word Association Mining and Analysis 3.1 Paradigmatic Relation Discovery ㆍParadigmatic Relation A & B have paradigmatic relation if they can be substituted for each other (i.e., A & B are in the same class) 3.2. Syntagmatic Relation Discovery In semiotics, syntagmatic analysis is analysis of syntax or surface structure (syntagmatic structure) as opposed to paradigms (paradigmatic analysis). This is often achieved using commutation tests....