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[Coursera] Machine Learning by Andrew Ng
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2016-8-4 13:36
2024-8-25 17:45
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Coursera
Machine
Learning
by
Andrew
Ng
文件列表
01. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp4
11.95MB
01. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).mp4
9.35MB
01. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp4
13.45MB
01. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp4
16.66MB
02. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp4
9MB
02. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp4
9.05MB
02. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp4
12.24MB
02. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp4
11.36MB
02. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp4
13.5MB
02. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp4
13.03MB
02. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp4
12.18MB
02. Linear Regression with One Variable (Week 1)/2 - 8 - What-'s Next (6 min).mp4
6.08MB
03. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp4
9.56MB
03. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp4
7.46MB
03. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp4
15MB
03. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp4
12.59MB
03. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp4
9.81MB
03. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp4
12.87MB
04. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp4
8.84MB
04. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4
5.78MB
04. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4
9.46MB
04. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4
9.26MB
04. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp4
8.26MB
04. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp4
17.13MB
04. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4
6.24MB
05. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp4
17.72MB
05. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp4
20.77MB
05. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp4
15.25MB
05. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp4
13.32MB
05. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).mp4
16.49MB
05. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp4
16.09MB
05. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4
5.46MB
06. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp4
8.77MB
06. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp4
8.34MB
06. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp4
16.74MB
06. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp4
13.09MB
06. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4
11.96MB
06. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp4
18.15MB
06. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).mp4
6.93MB
07. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp4
11.15MB
07. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp4
11.63MB
07. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp4
12MB
07. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp4
10.89MB
08. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp4
10.88MB
08. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp4
9.89MB
08. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp4
13.51MB
08. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp4
13.45MB
08. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp4
7.89MB
08. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp4
14MB
08. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp4
4.83MB
09. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp4
7.66MB
09. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp4
13.94MB
09. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp4
15.44MB
09. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp4
9.38MB
09. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp4
13.5MB
09. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp4
7.56MB
09. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp4
16.3MB
09. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp4
14.88MB
10. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp4
6.86MB
10. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp4
8.48MB
10. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp4
14.07MB
10. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4
8.97MB
10. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias-Variance (11 min).mp4
12.6MB
10. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp4
12.92MB
10. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp4
8.18MB
11. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp4
11.17MB
11. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp4
15.43MB
11. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4
13.25MB
11. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp4
15.99MB
11. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp4
12.87MB
12. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp4
16.65MB
12. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp4
11.81MB
12. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4
21.83MB
12. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp4
17.57MB
12. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp4
17.45MB
12. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp4
23.95MB
13. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).mp4
3.8MB
13. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp4
13.81MB
13. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp4
8.15MB
13. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp4
8.67MB
13. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp4
9.4MB
14. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).mp4
14.31MB
14. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).mp4
6.3MB
14. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4
10.45MB
14. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4
17.79MB
14. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp4
11.84MB
14. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp4
4.98MB
14. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp4
14.7MB
15. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp4
8.35MB
15. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp4
11.69MB
15. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp4
13.95MB
15. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4
15.15MB
15. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4
9.28MB
15. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp4
14.12MB
15. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4
15.93MB
15. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4
16.34MB
16. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp4
10.67MB
16. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp4
16.93MB
16. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp4
11.75MB
16. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp4
10.31MB
16. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp4
9.68MB
16. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).mp4
9.71MB
17. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp4
6.5MB
17. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp4
15.33MB
17. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp4
7.32MB
17. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4
13.33MB
17. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp4
14.91MB
17. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4
16.06MB
18. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp4
7.91MB
18. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp4
16.52MB
18. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4
18.82MB
18. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp4
16.11MB
19. Conclusion/19 - 1 - Summary and Thank You (5 min).mp4
6.09MB
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