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种子名称： [Coursera] Machine Learning by Andrew Ng
文件类型： 视频
文件数目： 113个文件
文件大小： 1.33 GB
收录时间： 2016-8-4 13:36
已经下载： 3次
资源热度： 609
最近下载： 2024-12-12 21:47

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[Coursera] Machine Learning by Andrew Ng.torrent

01. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp411.95MB

01. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).mp49.35MB

01. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp413.45MB

01. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp416.66MB

02. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp49MB

02. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp49.05MB

02. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp412.24MB

02. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp411.36MB

02. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp413.5MB

02. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp413.03MB

02. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp412.18MB

02. Linear Regression with One Variable (Week 1)/2 - 8 - What-'s Next (6 min).mp46.08MB

03. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp49.56MB

03. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp47.46MB

03. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp415MB

03. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp412.59MB

03. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp49.81MB

03. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp412.87MB

04. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp48.84MB

04. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp45.78MB

04. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp49.46MB

04. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp49.26MB

04. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp48.26MB

04. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp417.13MB

04. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp46.24MB

05. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp417.72MB

05. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp420.77MB

05. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp415.25MB

05. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp413.32MB

05. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).mp416.49MB

05. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp416.09MB

05. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp45.46MB

06. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp48.77MB

06. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp48.34MB

06. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp416.74MB

06. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp413.09MB

06. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp411.96MB

06. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp418.15MB

06. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).mp46.93MB

07. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp411.15MB

07. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp411.63MB

07. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp412MB

07. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp410.89MB

08. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp410.88MB

08. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp49.89MB

08. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp413.51MB

08. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp413.45MB

08. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp47.89MB

08. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp414MB

08. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp44.83MB

09. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp47.66MB

09. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp413.94MB

09. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp415.44MB

09. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp49.38MB

09. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp413.5MB

09. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp47.56MB

09. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp416.3MB

09. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp414.88MB

10. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp46.86MB

10. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp48.48MB

10. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp414.07MB

10. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp48.97MB

10. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias-Variance (11 min).mp412.6MB

10. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp412.92MB

10. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp48.18MB

11. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp411.17MB

11. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp415.43MB

11. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp413.25MB

11. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp415.99MB

11. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp412.87MB

12. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp416.65MB

12. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp411.81MB

12. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp421.83MB

12. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp417.57MB

12. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp417.45MB

12. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp423.95MB

13. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).mp43.8MB

13. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp413.81MB

13. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp48.15MB

13. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp48.67MB

13. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp49.4MB

14. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).mp414.31MB

14. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).mp46.3MB

14. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp410.45MB

14. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp417.79MB

14. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp411.84MB

14. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp44.98MB

14. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp414.7MB

15. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp48.35MB

15. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp411.69MB

15. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp413.95MB

15. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp415.15MB

15. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp49.28MB

15. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp414.12MB

15. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp415.93MB

15. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp416.34MB

16. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp410.67MB

16. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp416.93MB

16. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp411.75MB

16. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp410.31MB

16. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp49.68MB

16. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).mp49.71MB

17. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp46.5MB

17. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp415.33MB

17. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp47.32MB

17. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp413.33MB

17. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp414.91MB

17. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp416.06MB

18. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp47.91MB

18. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp416.52MB

18. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp418.82MB

18. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp416.11MB

19. Conclusion/19 - 1 - Summary and Thank You (5 min).mp46.09MB