DEEP LEARNING - Ian Goodfellow, Yoshua Bengio và Aaron Courville
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Giáo trình về Deep Learning của Ian Goodfellow, Yoshua Bengio và Aaron Courville, bao gồm kiến thức nền tảng toán học, học máy và các kỹ thuật deep learning hiện đại.
- Dokumentenname
- DEEP LEARNING - Ian Goodfellow, Yoshua Bengio và Aaron Courville
- Schule / Kurs
- Helwan University · Deep learning
- Inhalt
- Tài liệu này trình bày chi tiết về học sâu, bắt đầu từ các khái niệm toán học và học máy cơ bản, sau đó đi sâu vào các mạng nơ-ron sâu hiện đại và các phương pháp thực hành.
- Inhaltsverzeichnis
- Contents
- Website
- Acknowledgments
- Notation
- 1 Introduction
- 1.1 Who Should Read This Book? . . . . . . . . . . . . . . . . . . . .
- 1.2 Historical Trends in Deep Learning . . . . . . . . . . . . . . . . .
- I Applied Math and Machine Learning Basics
- 2 Linear Algebra
- 2.1 Scalars, Vectors, Matrices and Tensors . . . . . . . . . . . . . . .
- 2.2 Multiplying Matrices and Vectors . . . . . . . . . . . . . . . . . .
- 2.3 Identity and Inverse Matrices . . . . . . . . . . . . . . . . . . . .
- 2.4 Linear Dependence and Span . . . . . . . . . . . . . . . . . . . .
- 2.5 Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.6 Special Kinds of Matrices and Vectors . . . . . . . . . . . . . . .
- 2.7 Eigendecomposition . . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.8 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . .
- 2.9 The Moore-Penrose Pseudoinverse . . . . . . . . . . . . . . . . . .
- 2.10 The Trace Operator . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.11 The Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.12 Example: Principal Components Analysis . . . . . . . . . . . . .
- 3 Probability and Information Theory
- 3.1 Why Probability? . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.2 Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.3 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . .
- 3.4 Marginal Probability . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.5 Conditional Probability . . . . . . . . . . . . . . . . . . . . . . .
- 3.6 The Chain Rule of Conditional Probabilities . . . . . . . . . . . .
- 3.7 Independence and Conditional Independence . . . . . . . . . . . .
- 3.8 Expectation, Variance and Covariance . . . . . . . . . . . . . . .
- 3.9 Common Probability Distributions . . . . . . . . . . . . . . . . .
- 3.10 Useful Properties of Common Functions . . . . . . . . . . . . . .
- 3.11 Bayes’ Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.12 Technical Details of Continuous Variables . . . . . . . . . . . .
- 3.13 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.14 Structured Probabilistic Models . . . . . . . . . . . . . . . . . . .
- 4 Numerical Computation
- 4.1 Overflow and Underflow . . . . . . . . . . . . . . . . . . . . . . .
- 4.2 Poor Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . .
- 4.3 Gradient-Based Optimization . . . . . . . . . . . . . . . . . . . .
- 4.4 Constrained Optimization . . . . . . . . . . . . . . . . . . . . . .
- 4.5 Example: Linear Least Squares . . . . . . . . . . . . . . . . . . .
- 5 Machine Learning Basics
- 5.1 Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . .
- 5.2 Capacity, Overfitting and Underfitting . . . . . . . . . . . . . . .
- 5.3 Hyperparameters and Validation Sets . . . . . . . . . . . . . . .
- 5.4 Estimators, Bias and Variance . . . . . . . . . . . . . . . . . . .
- 5.5 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . .
- 5.6 Bayesian Statistics . . . . . . . . . . . . . . . . . . . . . . . . .
- 5.7 Supervised Learning Algorithms . . . . . . . . . . . . . . . . . .
- 5.8 Unsupervised Learning Algorithms . . . . . . . . . . . . . . . . .
- 5.9 Stochastic Gradient Descent . . . . . . . . . . . . . . . . . . . .
- 5.10 Building a Machine Learning Algorithm . . . . . . . . . . . . . .
- 5.11 Challenges Motivating Deep Learning . . . . . . . . . . . . . . .
- II Deep Networks: Modern Practices
- 6 Deep Feedforward Networks
- 6.1 Example: Learning XOR . . . . . . . . . . . . . . . . . . . . . . .
- 6.2 Gradient-Based Learning . . . . . . . . . . . . . . . . . . . . . . .
- 6.3 Hidden Units . . . . . . . . . . . . . . . . . . . . .
- 6.4 . . .
- Seiten
- 802 Seiten
- Hochgeladen von
- Uni24h
Beschreibung
Trích nội dung tài liệu
Deep Learning Ian Goodfellow Yoshua Bengio Aaron Courville Contents Website Acknowledgments Notation Introduction 1.1 Who Should Read This Book? . . . . . . . . . . . . . . . . . . . . 1.2 Historical Trends in Deep Learning . . . . . . . . . . . . . . . . . Applied Math and Machine Learning Basics Linear Algebra 2.1 Scalars, Vectors, Matrices and Tensors . . . . . . . . . . . . . . . 2.2 Multiplying Matrices and Vectors . . . . . . . . . . . . . . . . . . 2.3 Identity and Inverse Matrices . . . . . . . . . . . . . . . . . . . . 2.4 Linear Dependence and Span . . . . . . . . . . . . . . . . . . . . 2.5 Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Special Kinds of Matrices and Vectors . . . . . . . . . . . . . . . 2.7 Eigendecomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . 2.9 The Moore-Penrose Pseudoinverse . . . . . . . . . . . . . . . . . . 2.10 The Trace Operator . . . . . . . . . . . . . . . . . . . . . . . . . 2.11 The Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.12 Example: Principal Components Analysis . . . . . . . . . . . . . Probability and Information Theory 3.1 Why Probability? . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 CONTENTS Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . Marginal Probability . . . . . . . . . . . . . . . . . . . . . . . . . Conditional Probability . . . . . . . . . . . . . . . . . . . . . . . The Chain Rule of Conditional Probabilities . . . . . . . . . . . . Independence and Conditional Independence . . . . . . . . . . . . Expectation, Variance and Covariance . . . . . . . . . . . . . . . Common Probability Distributions . . . . . . . . .
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DEEP LEARNING - Ian Goodfellow, Yoshua Bengio và Aaron Courville
Vorschau wird generiert...
Trích nội dung tài liệu
Deep Learning Ian Goodfellow Yoshua Bengio Aaron Courville Contents Website Acknowledgments Notation Introduction 1.1 Who Should Read This Book? . . . . . . . . . . . . . . . . . . . . 1.2 Historical Trends in Deep Learning . . . . . . . . . . . . . . . . . Applied Math and Machine Learning Basics Linear Algebra 2.1 Scalars, Vectors, Matrices and Tensors . . . . . . . . . . . . . . . 2.2 Multiplying Matrices and Vectors . . . . . . . . . . . . . . . . . . 2.3 Identity and Inverse Matrices . . . . . . . . . . . . . . . . . . . . 2.4 Linear Dependence and Span . . . . . . . . . . . . . . . . . . . . 2.5 Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Special Kinds of Matrices and Vectors . . . . . . . . . . . . . . . 2.7 Eigendecomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . 2.9 The Moore-Penrose Pseudoinverse . . . . . . . . . . . . . . . . . . 2.10 The Trace Operator . . . . . . . . . . . . . . . . . . . . . . . . . 2.11 The Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.12 Example: Principal Components Analysis . . . . . . . . . . . . . Probability and Information Theory 3.1 Why Probability? . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 CONTENTS Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . Marginal Probability . . . . . . . . . . . . . . . . . . . . . . . . . Conditional Probability . . . . . . . . . . . . . . . . . . . . . . . The Chain Rule of Conditional Probabilities . . . . . . . . . . . . Independence and Conditional Independence . . . . . . . . . . . . Expectation, Variance and Covariance . . . . . . . . . . . . . . . Common Probability Distributions . . . . . . . . .
- Dokumentenname
- DEEP LEARNING - Ian Goodfellow, Yoshua Bengio và Aaron Courville
- Schule / Kurs
- Helwan University · Deep learning
- Inhalt
- Tài liệu này trình bày chi tiết về học sâu, bắt đầu từ các khái niệm toán học và học máy cơ bản, sau đó đi sâu vào các mạng nơ-ron sâu hiện đại và các phương pháp thực hành.
- Inhaltsverzeichnis
- Contents
- Website
- Acknowledgments
- Notation
- 1 Introduction
- 1.1 Who Should Read This Book? . . . . . . . . . . . . . . . . . . . .
- 1.2 Historical Trends in Deep Learning . . . . . . . . . . . . . . . . .
- I Applied Math and Machine Learning Basics
- 2 Linear Algebra
- 2.1 Scalars, Vectors, Matrices and Tensors . . . . . . . . . . . . . . .
- 2.2 Multiplying Matrices and Vectors . . . . . . . . . . . . . . . . . .
- 2.3 Identity and Inverse Matrices . . . . . . . . . . . . . . . . . . . .
- 2.4 Linear Dependence and Span . . . . . . . . . . . . . . . . . . . .
- 2.5 Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.6 Special Kinds of Matrices and Vectors . . . . . . . . . . . . . . .
- 2.7 Eigendecomposition . . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.8 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . .
- 2.9 The Moore-Penrose Pseudoinverse . . . . . . . . . . . . . . . . . .
- 2.10 The Trace Operator . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.11 The Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 2.12 Example: Principal Components Analysis . . . . . . . . . . . . .
- 3 Probability and Information Theory
- 3.1 Why Probability? . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.2 Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.3 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . .
- 3.4 Marginal Probability . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.5 Conditional Probability . . . . . . . . . . . . . . . . . . . . . . .
- 3.6 The Chain Rule of Conditional Probabilities . . . . . . . . . . . .
- 3.7 Independence and Conditional Independence . . . . . . . . . . . .
- 3.8 Expectation, Variance and Covariance . . . . . . . . . . . . . . .
- 3.9 Common Probability Distributions . . . . . . . . . . . . . . . . .
- 3.10 Useful Properties of Common Functions . . . . . . . . . . . . . .
- 3.11 Bayes’ Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.12 Technical Details of Continuous Variables . . . . . . . . . . . .
- 3.13 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . .
- 3.14 Structured Probabilistic Models . . . . . . . . . . . . . . . . . . .
- 4 Numerical Computation
- 4.1 Overflow and Underflow . . . . . . . . . . . . . . . . . . . . . . .
- 4.2 Poor Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . .
- 4.3 Gradient-Based Optimization . . . . . . . . . . . . . . . . . . . .
- 4.4 Constrained Optimization . . . . . . . . . . . . . . . . . . . . . .
- 4.5 Example: Linear Least Squares . . . . . . . . . . . . . . . . . . .
- 5 Machine Learning Basics
- 5.1 Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . .
- 5.2 Capacity, Overfitting and Underfitting . . . . . . . . . . . . . . .
- 5.3 Hyperparameters and Validation Sets . . . . . . . . . . . . . . .
- 5.4 Estimators, Bias and Variance . . . . . . . . . . . . . . . . . . .
- 5.5 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . .
- 5.6 Bayesian Statistics . . . . . . . . . . . . . . . . . . . . . . . . .
- 5.7 Supervised Learning Algorithms . . . . . . . . . . . . . . . . . .
- 5.8 Unsupervised Learning Algorithms . . . . . . . . . . . . . . . . .
- 5.9 Stochastic Gradient Descent . . . . . . . . . . . . . . . . . . . .
- 5.10 Building a Machine Learning Algorithm . . . . . . . . . . . . . .
- 5.11 Challenges Motivating Deep Learning . . . . . . . . . . . . . . .
- II Deep Networks: Modern Practices
- 6 Deep Feedforward Networks
- 6.1 Example: Learning XOR . . . . . . . . . . . . . . . . . . . . . . .
- 6.2 Gradient-Based Learning . . . . . . . . . . . . . . . . . . . . . . .
- 6.3 Hidden Units . . . . . . . . . . . . . . . . . . . . .
- 6.4 . . .
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Blind vs Heuristic Search Strategies Sheets 1 to 4 (Lecture 4) (Giải quyết vấn đề bằng tìm kiếm)
Artificial Intelligence II - Applied Machine Learning (Lecture 2) (Cây quyết định ID3 và ứng dụng trí tuệ nhân tạo trong kinh doanh)
Software Engineering I (Lectures 0 to 9) (Kỹ sư phần mềm I)
Intro to Ensemble Learning (Lecture 7) (Cơ bản về học tập hợp thành)
Deep Learning cơ bản V2 - Nguyễn Thanh Tuấn
Tổng hợp Đề Toán 5 - Luyện thi vào Lớp 6 - CLB EMath
Bài giảng vật lý đại cương (Chương 3) - Đỗ Ngọc Uấn
Chương 8.Nguyên tử - Vật lý đại cương 3 - TS.Nguyễn Thị Trang
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
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