斯坦福大学 机器学习 吴恩达

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├─pdf
│      Lecture1.pdf
│      Lecture10.pdf
│      Lecture11.pdf
│      Lecture12.pdf
│      Lecture13.pdf
│      Lecture14.pdf
│      Lecture15.pdf
│      Lecture16.pdf
│      Lecture17.pdf
│      Lecture18.pdf
│      Lecture2.pdf
│      Lecture3.pdf
│      Lecture4.pdf
│      Lecture5.pdf
│      Lecture6.pdf
│      Lecture7.pdf
│      Lecture8.pdf
│      Lecture9.pdf
│      
├─ppt
│      Lecture1.pptx
│      Lecture10.pptx
│      Lecture11.pptx
│      Lecture12.pptx
│      Lecture13.pptx
│      Lecture14.pptx
│      Lecture15.pptx
│      Lecture16.pptx
│      Lecture17.pptx
│      Lecture18.pptx
│      Lecture2.pptx
│      Lecture3.pptx
│      Lecture4.pptx
│      Lecture5.pptx
│      Lecture6.pptx
│      Lecture7.pptx
│      Lecture8.pptx
│      Lecture9.pptx
│  └─整合pdf
│          ex1.pdf
│          ex2.pdf
│          ex3.pdf
│          ex4.pdf
│          ex5.pdf
│          ex6.pdf
│          ex7.pdf
│          ex8.pdf
│          Programming Exercise(机器学习2014练习).pdf
│          源代码打印.pdf
│          源代码目录.docx
│         
└─视频
        1 - 1 - Welcome (7 min).mkv
        1 - 1 - Welcome (7 min).srt
        1 - 2 - What is Machine Learning_ (7 min).mkv
        1 - 2 - What is Machine Learning_ (7 min).srt
        1 - 3 - Supervised Learning (12 min).mkv
        1 - 3 - Supervised Learning (12 min).srt
        1 - 4 - Unsupervised Learning (14 min).mkv
        1 - 4 - Unsupervised Learning (14 min).srt
        2 - 1 - Model Representation (8 min).mkv
        2 - 1 - Model Representation (8 min).srt
        2 - 2 - Cost Function (8 min).mkv
        2 - 2 - Cost Function (8 min).srt
        2 - 3 - Cost Function - Intuition I (11 min).mkv
        2 - 3 - Cost Function - Intuition I (11 min).srt
        2 - 4 - Cost Function - Intuition II (9 min).mkv
        2 - 4 - Cost Function - Intuition II (9 min).srt
        2 - 5 - Gradient Descent (11 min).mkv
        2 - 5 - Gradient Descent (11 min).srt
        2 - 6 - Gradient Descent Intuition (12 min).mkv
        2 - 6 - Gradient Descent Intuition (12 min).srt
        2 - 7 - Gradient Descent For Linear Regression (10 min).srt
        2 - 7 - GradientDescentForLinearRegression  (6 min).mkv
        2 - 8 - What_'s Next (6 min).mkv
        2 - 8 - What_'s Next (6 min).srt
        3 - 1 - Matrices and Vectors (9 min).mkv
        3 - 1 - Matrices and Vectors (9 min).srt
        3 - 2 - Addition and Scalar Multiplication (7 min).mkv
        3 - 2 - Addition and Scalar Multiplication (7 min).srt
        3 - 3 - Matrix Vector Multiplication (14 min).mkv
        3 - 3 - Matrix Vector Multiplication (14 min).srt
        3 - 4 - Matrix Matrix Multiplication (11 min).mkv
        3 - 4 - Matrix Matrix Multiplication (11 min).srt
        3 - 5 - Matrix Multiplication Properties (9 min).mkv
        3 - 5 - Matrix Multiplication Properties (9 min).srt
        3 - 6 - Inverse and Transpose (11 min).mkv
        3 - 6 - Inverse and Transpose (11 min).srt
        4 - 1 - Multiple Features (8 min).mkv
        4 - 1 - Multiple Features (8 min).srt
        4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv
        4 - 2 - Gradient Descent for Multiple Variables (5 min).srt
        4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv
        4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt
        4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
        4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt
        4 - 5 - Features and Polynomial Regression (8 min).mkv
        4 - 5 - Features and Polynomial Regression (8 min).srt
        4 - 6 - Normal Equation (16 min).mkv
        4 - 6 - Normal Equation (16 min).srt
        4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv
        4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).srt
        5 - 1 - Basic Operations (14 min).mkv
        5 - 1 - Basic Operations (14 min).srt
        5 - 2 - Moving Data Around (16 min).mkv
        5 - 2 - Moving Data Around (16 min).srt
        5 - 3 - Computing on Data (13 min).mkv
        5 - 3 - Computing on Data (13 min).srt
        5 - 4 - Plotting Data (10 min).mkv
        5 - 4 - Plotting Data (10 min).srt
        5 - 5 - Control Statements_ for, while, if statements (13 min).mkv
        5 - 5 - Control Statements_ for, while, if statements (13 min).srt
        5 - 6 - Vectorization (14 min).mkv
        5 - 6 - Vectorization (14 min).srt
        5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv
        5 - 7 - Working on and Submitting Programming Exercises (4 min).srt
        6 - 1 - Classification (8 min).mkv
        6 - 1 - Classification (8 min).srt
        6 - 2 - Hypothesis Representation (7 min).mkv
        6 - 2 - Hypothesis Representation (7 min).srt
        6 - 3 - Decision Boundary (15 min).mkv
        6 - 3 - Decision Boundary (15 min).srt
        6 - 4 - Cost Function (11 min).mkv
        6 - 4 - Cost Function (11 min).srt
        6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv
        6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt
        6 - 6 - Advanced Optimization (14 min).mkv
        6 - 6 - Advanced Optimization (14 min).srt
        6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv
        6 - 7 - Multiclass Classification_ One-vs-all (6 min).srt
        7 - 1 - The Problem of Overfitting (10 min).mkv
        7 - 1 - The Problem of Overfitting (10 min).srt
        7 - 2 - Cost Function (10 min).mkv
        7 - 2 - Cost Function (10 min).srt
        7 - 3 - Regularized Linear Regression (11 min).mkv
        7 - 3 - Regularized Linear Regression (11 min).srt
        7 - 4 - Regularized Logistic Regression (9 min).mkv
        7 - 4 - Regularized Logistic Regression (9 min).srt
        8 - 1 - Non-linear Hypotheses (10 min).mkv
        8 - 1 - Non-linear Hypotheses (10 min).srt
        8 - 2 - Neurons and the Brain (8 min).mkv
        8 - 2 - Neurons and the Brain (8 min).srt
        8 - 3 - Model Representation I (12 min).mkv
        8 - 3 - Model Representation I (12 min).srt
        8 - 4 - Model Representation II (12 min).mkv
        8 - 4 - Model Representation II (12 min).srt
        8 - 5 - Examples and Intuitions I (7 min).mkv
        8 - 5 - Examples and Intuitions I (7 min).srt
        8 - 6 - Examples and Intuitions II (10 min).mkv
        8 - 6 - Examples and Intuitions II (10 min).srt
        8 - 7 - Multiclass Classification (4 min).mkv
        8 - 7 - Multiclass Classification (4 min).srt
        9 - 1 - Cost Function (7 min).mkv
        9 - 1 - Cost Function (7 min).srt
        9 - 2 - Backpropagation Algorithm (12 min).mkv
        9 - 2 - Backpropagation Algorithm (12 min).srt
        9 - 3 - Backpropagation Intuition (13 min).mkv
        9 - 3 - Backpropagation Intuition (13 min).srt
        9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv
        9 - 4 - Implementation Note_ Unrolling Parameters (8 min).srt
        9 - 5 - Gradient Checking (12 min).mkv
        9 - 5 - Gradient Checking (12 min).srt
        9 - 6 - Random Initialization (7 min).mkv
        9 - 6 - Random Initialization (7 min).srt
        9 - 7 - Putting It Together (14 min).mkv
        9 - 7 - Putting It Together (14 min).srt
        9 - 8 - Autonomous Driving (7 min).mkv
        9 - 8 - Autonomous Driving (7 min).srt
        10 - 1 - Deciding What to Try Next (6 min).mkv
        10 - 1 - Deciding What to Try Next (6 min).srt
        10 - 2 - Evaluating a Hypothesis (8 min).mkv
        10 - 2 - Evaluating a Hypothesis (8 min).srt
        10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
        10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt
        10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv
        10 - 4 - Diagnosing Bias vs. Variance (8 min).srt
        10 - 5 - Regularization and Bias_Variance (11 min).mkv
        10 - 5 - Regularization and Bias_Variance (11 min).srt
        10 - 6 - Learning Curves (12 min).mkv
        10 - 6 - Learning Curves (12 min).srt
        10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
        10 - 7 - Deciding What to Do Next Revisited (7 min).srt
        11 - 1 - Prioritizing What to Work On (10 min).mkv
        11 - 1 - Prioritizing What to Work On (10 min).srt
        11 - 2 - Error Analysis (13 min).mkv
        11 - 2 - Error Analysis (13 min).srt
        11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
        11 - 3 - Error Metrics for Skewed Classes (12 min).srt
        11 - 4 - Trading Off Precision and Recall (14 min).mkv
        11 - 4 - Trading Off Precision and Recall (14 min).srt
        11 - 5 - Data For Machine Learning (11 min).mkv
        11 - 5 - Data For Machine Learning (11 min).srt
        12 - 1 - Optimization Objective (15 min).mkv
        12 - 1 - Optimization Objective (15 min).srt
        12 - 2 - Large Margin Intuition (11 min).mkv
        12 - 2 - Large Margin Intuition (11 min).srt
        12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
        12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).srt
        12 - 4 - Kernels I (16 min).mkv
        12 - 4 - Kernels I (16 min).srt
        12 - 5 - Kernels II (16 min).mkv
        12 - 5 - Kernels II (16 min).srt
        12 - 6 - Using An SVM (21 min).mkv
        12 - 6 - Using An SVM (21 min).srt
        13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv
        13 - 1 - Unsupervised Learning_ Introduction (3 min).srt
        13 - 2 - K-Means Algorithm (13 min).mkv
        13 - 2 - K-Means Algorithm (13 min).srt
        13 - 3 - Optimization Objective (7 min)(1).mkv
        13 - 3 - Optimization Objective (7 min)(1).srt
        13 - 3 - Optimization Objective (7 min).mkv
        13 - 3 - Optimization Objective (7 min).srt
        13 - 4 - Random Initialization (8 min).mkv
        13 - 4 - Random Initialization (8 min).srt
        13 - 5 - Choosing the Number of Clusters (8 min).mkv
        13 - 5 - Choosing the Number of Clusters (8 min).srt
        14 - 1 - Motivation I_ Data Compression (10 min).mkv
        14 - 1 - Motivation I_ Data Compression (10 min).srt
        14 - 2 - Motivation II_ Visualization (6 min).mkv
        14 - 2 - Motivation II_ Visualization (6 min).srt
        14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
        14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt
        14 - 4 - Principal Component Analysis Algorithm (15 min).mkv
        14 - 4 - Principal Component Analysis Algorithm (15 min).srt
        14 - 5 - Choosing the Number of Principal Components (11 min).mkv
        14 - 5 - Choosing the Number of Principal Components (11 min).srt
        14 - 6 - Reconstruction from Compressed Representation (4 min).mkv
        14 - 6 - Reconstruction from Compressed Representation (4 min).srt
        14 - 7 - Advice for Applying PCA (13 min).mkv
        14 - 7 - Advice for Applying PCA (13 min).srt
        15 - 1 - Problem Motivation (8 min).mkv
        15 - 1 - Problem Motivation (8 min).srt
        15 - 2 - Gaussian Distribution (10 min).mkv
        15 - 2 - Gaussian Distribution (10 min).srt
        15 - 3 - Algorithm (12 min).mkv
        15 - 3 - Algorithm (12 min).srt
        15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
        15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).srt
        15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
        15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt
        15 - 6 - Choosing What Features to Use (12 min).mkv
        15 - 6 - Choosing What Features to Use (12 min).srt
        15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv
        15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).srt
        15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv
        15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).srt
        16 - 1 - Problem Formulation (8 min).mkv
        16 - 1 - Problem Formulation (8 min).srt
        16 - 2 - Content Based Recommendations (15 min).mkv
        16 - 2 - Content Based Recommendations (15 min).srt
        16 - 3 - Collaborative Filtering (10 min).mkv
        16 - 3 - Collaborative Filtering (10 min).srt
        16 - 4 - Collaborative Filtering Algorithm (9 min).mkv
        16 - 4 - Collaborative Filtering Algorithm (9 min).srt
        16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
        16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).srt
        16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv
        16 - 6 - Implementational Detail_ Mean Normalization (9 min).srt
        17 - 1 - Learning With Large Datasets (6 min).mkv
        17 - 1 - Learning With Large Datasets (6 min).srt
        17 - 2 - Stochastic Gradient Descent (13 min).mkv
        17 - 2 - Stochastic Gradient Descent (13 min).srt
        17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
        17 - 3 - Mini-Batch Gradient Descent (6 min).srt
        17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv
        17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt
        17 - 5 - Online Learning (13 min).mkv
        17 - 5 - Online Learning (13 min).srt
        17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
        17 - 6 - Map Reduce and Data Parallelism (14 min).srt
        18 - 1 - Problem Description and Pipeline (7 min).mkv
        18 - 1 - Problem Description and Pipeline (7 min).srt
        18 - 2 - Sliding Windows (15 min).mkv
        18 - 2 - Sliding Windows (15 min).srt
        18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv
        18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt
        18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
        18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).srt
        19 - 1 - Summary and Thank You (5 min).mkv
        19 - 1 - Summary and Thank You (5 min).srt

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