Super cheatsheet machine learning (Khái niệm và công thức chính trong học máy) - Afshine Amidi and Shervine Amidi
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Tài liệu cheatsheet tổng hợp các khái niệm và công thức chính trong học máy, bao gồm học có giám sát, không giám sát, học sâu, mẹo và mẹo, và kiến thức toán cơ bản.
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https://stanford.edu/~shervine CS 229 – Machine Learning Super VIP Cheatsheet: Machine Learning Afshine Amidi and Shervine Amidi August 23, 2018 Contents 1 Supervised Learning 1.1 Introduction to Supervised Learning . . . . . . . . . . . . . . . . . . . 1.2 Notations and general concepts . . . . . . . . . . . . . . . . . . . . . 1.3 Linear models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Linear regression . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Classification and logistic regression . . . . . . . . . . . . . . . 1.3.3 Generalized Linear Models . . . . . . . . . . . . . . . . . . . . 1.4 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Generative Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Gaussian Discriminant Analysis . . . . . . . . . . . . . . . . . 1.5.2 Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Other non-parametric approaches . . . . . . . . . . . . . . . . . . . . 1.7 Learning Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2 2 2 3 3 3 4 4 4 4 4 2 Unsupervised Learning 2.1 Introduction to Unsupervised Learning . . . . . . . . . . . . . . . . . 2.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Expectation-Maximization . . . . . . . . . . . . . . . . . . . . 2.2.2 k-means clustering . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Hierarchical clustering . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Clustering assessment metrics . . . . . . . . . . . . . . . . . . 2.3 Dimension reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Principal component analysis . . . . . . . . . . . . . . . . . . 2.3.2 Independent component analysis . . . . . . . . . . . . . . . . . 5 5 5 5 6 6 6 6 6 7 3 Deep Learning 3.1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . 3.3 Rec
AI summary
- Document name
- Super cheatsheet machine learning (Khái niệm và công thức chính trong học máy) - Afshine Amidi and Shervine Amidi
- School / Course
- Stanford University · Machine learning
- Author (in document)
- Afshine Amidi and Shervine Amidi
- Content
- Đây là bản tóm tắt chi tiết về học máy, bao gồm các phương pháp học có giám sát, không giám sát, học sâu, cùng các kỹ thuật và kiến thức nền tảng cần thiết. Tài liệu được biên soạn cho khóa học CS 229 tại Stanford.
- Table of contents
- 1 Supervised Learning
- 1.1 Introduction to Supervised Learning
- 1.2 Notations and general concepts
- 1.3 Linear models
- 1.3.1 Linear regression
- 1.3.2 Classification and logistic regression
- 1.3.3 Generalized Linear Models
- 1.4 Support Vector Machines
- 1.5 Generative Learning
- 1.5.1 Gaussian Discriminant Analysis
- 1.5.2 Naive Bayes
- 1.6 Other non-parametric approaches
- 1.7 Learning Theory
- 2 Unsupervised Learning
- 2.1 Introduction to Unsupervised Learning
- 2.2 Clustering
- 2.2.1 Expectation-Maximization
- 2.2.2 k-means clustering
- 2.2.3 Hierarchical clustering
- 2.2.4 Clustering assessment metrics
- 2.3 Dimension reduction
- 2.3.1 Principal component analysis
- 2.3.2 Independent component analysis
- 3 Deep Learning
- 3.1 Neural Networks
- 3.2 Convolutional Neural Networks
- 3.3 Recurrent Neural Networks
- 3.4 Reinforcement Learning and Control
- 4 Machine Learning Tips and Tricks
- 4.1 Metrics
- 4.1.1 Classification
- 4.1.2 Regression
- 4.2 Model selection
- 4.3 Diagnostics
- 5 Refreshers
- 5.1 Probabilities and Statistics
- 5.1.1 Introduction to Probability and Combinatorics
- 5.1.2 Conditional Probability
- 5.1.3 Random Variables
- 5.1.4 Jointly Distributed Random Variables
- 5.1.5 Parameter estimation
- 5.2 Linear Algebra and Calculus
- 5.2.1 General notations
- 5.2.2 Matrix operations
- 5.2.3 Matrix properties
- 5.2.4 Matrix calculus
- Pages
- 15 pages
- Uploaded by
- Uni24h
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Super cheatsheet machine learning (Khái niệm và công thức chính trong học máy) - Afshine Amidi and Shervine Amidi
Generating preview...
https://stanford.edu/~shervine CS 229 – Machine Learning Super VIP Cheatsheet: Machine Learning Afshine Amidi and Shervine Amidi August 23, 2018 Contents 1 Supervised Learning 1.1 Introduction to Supervised Learning . . . . . . . . . . . . . . . . . . . 1.2 Notations and general concepts . . . . . . . . . . . . . . . . . . . . . 1.3 Linear models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Linear regression . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Classification and logistic regression . . . . . . . . . . . . . . . 1.3.3 Generalized Linear Models . . . . . . . . . . . . . . . . . . . . 1.4 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Generative Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Gaussian Discriminant Analysis . . . . . . . . . . . . . . . . . 1.5.2 Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Other non-parametric approaches . . . . . . . . . . . . . . . . . . . . 1.7 Learning Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2 2 2 3 3 3 4 4 4 4 4 2 Unsupervised Learning 2.1 Introduction to Unsupervised Learning . . . . . . . . . . . . . . . . . 2.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Expectation-Maximization . . . . . . . . . . . . . . . . . . . . 2.2.2 k-means clustering . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Hierarchical clustering . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Clustering assessment metrics . . . . . . . . . . . . . . . . . . 2.3 Dimension reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Principal component analysis . . . . . . . . . . . . . . . . . . 2.3.2 Independent component analysis . . . . . . . . . . . . . . . . . 5 5 5 5 6 6 6 6 6 7 3 Deep Learning 3.1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . 3.3 Rec
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- Document name
- Super cheatsheet machine learning (Khái niệm và công thức chính trong học máy) - Afshine Amidi and Shervine Amidi
- School / Course
- Stanford University · Machine learning
- Author (in document)
- Afshine Amidi and Shervine Amidi
- Content
- Đây là bản tóm tắt chi tiết về học máy, bao gồm các phương pháp học có giám sát, không giám sát, học sâu, cùng các kỹ thuật và kiến thức nền tảng cần thiết. Tài liệu được biên soạn cho khóa học CS 229 tại Stanford.
- Table of contents
- 1 Supervised Learning
- 1.1 Introduction to Supervised Learning
- 1.2 Notations and general concepts
- 1.3 Linear models
- 1.3.1 Linear regression
- 1.3.2 Classification and logistic regression
- 1.3.3 Generalized Linear Models
- 1.4 Support Vector Machines
- 1.5 Generative Learning
- 1.5.1 Gaussian Discriminant Analysis
- 1.5.2 Naive Bayes
- 1.6 Other non-parametric approaches
- 1.7 Learning Theory
- 2 Unsupervised Learning
- 2.1 Introduction to Unsupervised Learning
- 2.2 Clustering
- 2.2.1 Expectation-Maximization
- 2.2.2 k-means clustering
- 2.2.3 Hierarchical clustering
- 2.2.4 Clustering assessment metrics
- 2.3 Dimension reduction
- 2.3.1 Principal component analysis
- 2.3.2 Independent component analysis
- 3 Deep Learning
- 3.1 Neural Networks
- 3.2 Convolutional Neural Networks
- 3.3 Recurrent Neural Networks
- 3.4 Reinforcement Learning and Control
- 4 Machine Learning Tips and Tricks
- 4.1 Metrics
- 4.1.1 Classification
- 4.1.2 Regression
- 4.2 Model selection
- 4.3 Diagnostics
- 5 Refreshers
- 5.1 Probabilities and Statistics
- 5.1.1 Introduction to Probability and Combinatorics
- 5.1.2 Conditional Probability
- 5.1.3 Random Variables
- 5.1.4 Jointly Distributed Random Variables
- 5.1.5 Parameter estimation
- 5.2 Linear Algebra and Calculus
- 5.2.1 General notations
- 5.2.2 Matrix operations
- 5.2.3 Matrix properties
- 5.2.4 Matrix calculus
- Pages
- 15 pages
- Uploaded by
- Uni24h
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Eval algo notes (11) (Các kiểm định thống kê và so sánh thuật toán) - Sebastian Raschka
Refresher probabilities statistics (Xác suất và thống kê cho Machine Learning) - Afshine Amidi and Shervine Amidi
Ensembles notes (07) (Các phương pháp học ensemble) - Sebastian Raschka
Eval ci notes (09) (Đánh giá mô hình, khoảng tin cậy và kỹ thuật resampling) - Sebastian Raschka
Cheatsheet Deep Learning (Khái niệm chính về học sâu) - Afshine Amidi and Shervine Amidi
Chương 7.Cơ học lượng tử - Vật lý đại cương 3 - TS.Nguyễn Thị Trang
Chương 6.Quang học lượng tử - Vật lý đại cương 3 - TS.Nguyễn Thị Trang
Chương 5.Thuyết tương đối - Vật lý đại cương 3 - TS.Nguyễn Thị Trang
Chương 4. Tán xạ ánh sáng - Vật lý đại cương 3 - TS.Nguyễn Thị Trang
Chương 3.Phân cực ánh sáng - Vật lý đại cương 3 - TS.Nguyễn Thị Trang
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