Coursera-ML - TorBT

File Name

Size

avatar.png

55 kB

I. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp4

12 MB

I. Introduction (Week 1)/1 - 1 - Welcome (7 min).srt

9.9 kB

I. Introduction (Week 1)/1 - 2 - What is Machine Learning (7 min).mp4

9.4 MB

I. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).srt

10 kB

I. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp4

14 MB

I. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).srt

17 kB

I. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp4

17 MB

I. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).srt

29 kB

I. Introduction (Week 1)/docs_slides_Lecture1.pdf

3.3 MB

I. Introduction (Week 1)/docs_slides_Lecture1.pptx

4.0 MB

II. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp4

9.0 MB

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

9.9 kB

II. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp4

9.0 MB

II. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).srt

9.9 kB

II. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp4

12 MB

II. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).srt

12 kB

II. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp4

11 MB

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

11 kB

II. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp4

14 MB

II. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).srt

15 kB

II. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp4

13 MB

II. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).srt

16 kB

II. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp4

12 MB

II. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).srt

19 kB

II. Linear Regression with One Variable (Week 1)/2 - 8 - What's Next (6 min).srt

8.5 kB

II. Linear Regression with One Variable (Week 1)/2 - 8 - Whats Next (6 min).mp4

6.1 MB

II. Linear Regression with One Variable (Week 1)/docs_slides_Lecture2.pdf

2.9 MB

II. Linear Regression with One Variable (Week 1)/docs_slides_Lecture2.pptx

5.4 MB

III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp4

9.6 MB

III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).srt

16 kB

III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).txt

7.1 kB

III. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp4

7.5 MB

III. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).srt

12 kB

III. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp4

15 MB

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

24 kB

III. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp4

13 MB

III. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).srt

21 kB

III. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp4

9.8 MB

III. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).srt

17 kB

III. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp4

13 MB

III. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).srt

21 kB

III. Linear Algebra Review (Week 1, Optional)/docs_slides_Lecture3.pdf

1.8 MB

III. Linear Algebra Review (Week 1, Optional)/docs_slides_Lecture3.pptx

4.9 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp4

8.8 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).srt

14 kB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4

5.8 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).srt

6.6 kB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4

9.5 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt

17 kB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4

9.3 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt

18 kB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp4

8.3 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).srt

16 kB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp4

17 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).srt

31 kB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4

6.2 MB

IV. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).srt

9.8 kB

IV. Linear Regression with Multiple Variables (Week 2)/docs_slides_Lecture4.pdf

1.7 MB

IV. Linear Regression with Multiple Variables (Week 2)/docs_slides_Lecture4.pptx

4.4 MB

IV. Linear Regression with Multiple Variables (Week 2)/ex1.zip

470 kB

IX. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp4

7.7 MB

IX. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).srt

13 kB

IX. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp4

14 MB

IX. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).srt

23 kB

IX. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp4

15 MB

IX. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).srt

25 kB

IX. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note Unrolling Parameters (8 min).mp4

9.4 MB

IX. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).srt

15 kB

IX. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp4

14 MB

IX. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).srt

24 kB

IX. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp4

7.6 MB

IX. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).srt

14 kB

IX. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp4

16 MB

IX. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).srt

28 kB

IX. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp4

15 MB

IX. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).srt

9.8 kB

IX. Neural Networks Learning (Week 5)/docs_slides_Lecture9.pdf

3.4 MB

IX. Neural Networks Learning (Week 5)/docs_slides_Lecture9.pptx

5.0 MB

IX. Neural Networks Learning (Week 5)/ex4.zip

7.6 MB

V. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp4

18 MB

V. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).srt

25 kB

V. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp4

21 MB

V. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).srt

29 kB

V. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp4

15 MB

V. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).srt

25 kB

V. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp4

13 MB

V. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).srt

17 kB

V. Octave Tutorial (Week 2)/5 - 5 - Control Statements for while if statements (13 min).mp4

16 MB

V. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).srt

23 kB

V. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp4

16 MB

V. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).srt

25 kB

V. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4

5.5 MB

V. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).srt

4.4 kB

V. Octave Tutorial (Week 2)/docs_slides_Lecture5.pdf

242 kB

V. Octave Tutorial (Week 2)/docs_slides_Lecture5.pptx

407 kB

VI. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp4

8.8 MB

VI. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).srt

16 kB

VI. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp4

8.3 MB

VI. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).srt

14 kB

VI. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp4

17 MB

VI. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).srt

27 kB

VI. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp4

13 MB

VI. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).srt

22 kB

VI. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4

12 MB

VI. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt

20 kB

VI. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp4

18 MB

VI. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).srt

28 kB

VI. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification One-vs-all (6 min).mp4

6.9 MB

VI. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).srt

13 kB

VI. Logistic Regression (Week 3)/docs_slides_Lecture6.pdf

2.1 MB

VI. Logistic Regression (Week 3)/docs_slides_Lecture6.pptx

3.8 MB

VII. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp4

11 MB

VII. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).srt

19 kB

VII. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp4

12 MB

VII. Regularization (Week 3)/7 - 2 - Cost Function (10 min).srt

20 kB

VII. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp4

12 MB

VII. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).srt

20 kB

VII. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp4

11 MB

VII. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).srt

17 kB

VII. Regularization (Week 3)/docs_slides_Lecture7.pdf

2.3 MB

VII. Regularization (Week 3)/docs_slides_Lecture7.pptx

2.6 MB

VII. Regularization (Week 3)/ex2.zip

243 kB

VIII. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp4

11 MB

VIII. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).srt

19 kB

VIII. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp4

9.9 MB

VIII. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).srt

16 kB

VIII. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp4

14 MB

VIII. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).srt

22 kB

VIII. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp4

14 MB

VIII. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).srt

22 kB

VIII. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp4

7.9 MB

VIII. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).srt

13 kB

VIII. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp4

14 MB

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

17 kB

VIII. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp4

4.8 MB

VIII. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).srt

7.4 kB

VIII. Neural Networks Representation (Week 4)/docs_slides_Lecture8.pdf

5.0 MB

VIII. Neural Networks Representation (Week 4)/docs_slides_Lecture8.pptx

40 MB

VIII. Neural Networks Representation (Week 4)/ex3.zip

7.5 MB

X. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp4

6.9 MB

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

12 kB

X. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp4

8.5 MB

X. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).srt

12 kB

X. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mp4

14 MB

X. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt

25 kB

X. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4

9.0 MB

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

16 kB

X. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias_Variance (11 min).mp4

13 MB

X. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias_Variance (11 min).srt

22 kB

X. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp4

13 MB

X. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).srt

25 kB

X. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp4

8.2 MB

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

14 kB

X. Advice for Applying Machine Learning (Week 6)/docs_slides_Lecture10.pdf

1.5 MB

X. Advice for Applying Machine Learning (Week 6)/docs_slides_Lecture10.pptx

3.4 MB

X. Advice for Applying Machine Learning (Week 6)/ex5.zip

177 kB

XI. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp4

11 MB

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

20 kB

XI. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp4

15 MB

XI. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).srt

28 kB

XI. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4

13 MB

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

22 kB

XI. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp4

16 MB

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

29 kB

XI. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp4

13 MB

XI. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).srt

23 kB

XI. Machine Learning System Design (Week 6)/docs_slides_Lecture11.pdf

498 kB

XI. Machine Learning System Design (Week 6)/docs_slides_Lecture11.pptx

1.9 MB

XII. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp4

17 MB

XII. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).srt

29 kB

XII. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp4

12 MB

XII. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).srt

21 kB

XII. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4

22 MB

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

36 kB

XII. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp4

18 MB

XII. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).srt

29 kB

XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min) (1).mp4

17 MB

XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min) (1).srt

31 kB

XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp4

17 MB

XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).srt

31 kB

XII. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp4

24 MB

XII. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).srt

44 kB

XII. Support Vector Machines (Week 7)/docs_slides_Lecture12.pdf

2.3 MB

XII. Support Vector Machines (Week 7)/docs_slides_Lecture12.pptx

5.4 MB

XII. Support Vector Machines (Week 7)/ex6.zip

896 kB

XIII. Clustering (Week 8)/13 - 1 - Unsupervised Learning Introduction (3 min).mp4

3.8 MB

XIII. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).srt

7.0 kB

XIII. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp4

14 MB

XIII. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).srt

26 kB

XIII. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp4

8.1 MB

XIII. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).srt

14 kB

XIII. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp4

8.7 MB

XIII. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).srt

16 kB

XIII. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp4

9.4 MB

XIII. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).srt

18 kB

XIII. Clustering (Week 8)/docs_slides_Lecture13.pdf

2.2 MB

XIII. Clustering (Week 8)/docs_slides_Lecture13.pptx

2.8 MB

XIV. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I Data Compression (10 min).mp4

14 MB

XIV. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).srt

20 kB

XIV. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II Visualization (6 min).mp4

6.3 MB

XIV. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).srt

10 kB

XIV. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4

10 MB

XIV. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt

18 kB

XIV. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4

18 MB

XIV. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).srt

29 kB

XIV. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp4

12 MB

XIV. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).srt

21 kB

XIV. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp4

5.0 MB

XIV. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).srt

7.6 kB

XIV. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp4

15 MB

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

26 kB

XIV. Dimensionality Reduction (Week 8)/docs_slides_Lecture14.pdf

1.6 MB

XIV. Dimensionality Reduction (Week 8)/docs_slides_Lecture14.pptx

3.6 MB

XIV. Dimensionality Reduction (Week 8)/ex7.zip

11 MB

XIX. Conclusion/19 - 1 - Summary and Thank You (5 min).mp4

6.1 MB

XIX. Conclusion/19 - 1 - Summary and Thank You (5 min).srt

8.1 kB

XV. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp4

8.3 MB

XV. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).srt

16 kB

XV. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp4

12 MB

XV. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).srt

21 kB

XV. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp4

14 MB

XV. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).srt

24 kB

XV. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4

15 MB

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

27 kB

XV. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4

9.3 MB

XV. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt

16 kB

XV. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp4

14 MB

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

25 kB

XV. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4

16 MB

XV. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).srt

27 kB

XV. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4

16 MB

XV. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).srt

26 kB

XV. Anomaly Detection (Week 9)/docs_slides_Lecture15.pdf

3.3 MB

XV. Anomaly Detection (Week 9)/docs_slides_Lecture15.pptx

6.0 MB

XVI. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp4

11 MB

XVI. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).srt

17 kB

XVI. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp4

17 MB

XVI. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).srt

29 kB

XVI. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp4

12 MB

XVI. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).srt

20 kB

XVI. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp4

10 MB

XVI. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).srt

16 kB

XVI. Recommender Systems (Week 9)/16 - 5 - Vectorization Low Rank Matrix Factorization (8 min).mp4

9.7 MB

XVI. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).srt

16 kB

XVI. Recommender Systems (Week 9)/16 - 6 - Implementational Detail Mean Normalization (9 min).mp4

9.7 MB

XVI. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).srt

17 kB

XVI. Recommender Systems (Week 9)/docs_slides_Lecture16.pdf

1.4 MB

XVI. Recommender Systems (Week 9)/docs_slides_Lecture16.pptx

3.6 MB

XVI. Recommender Systems (Week 9)/ex8.zip

795 kB

XVII. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp4

6.5 MB

XVII. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).srt

7.9 kB

XVII. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp4

15 MB

XVII. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).srt

18 kB

XVII. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp4

7.3 MB

XVII. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).srt

7.8 kB

XVII. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4

13 MB

XVII. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt

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XVII. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp4

15 MB

XVII. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).srt

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XVII. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4

16 MB

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

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XVII. Large Scale Machine Learning (Week 10)/docs_slides_Lecture17.pdf

2.0 MB

XVII. Large Scale Machine Learning (Week 10)/docs_slides_Lecture17.pptx

3.8 MB

XVIII. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp4

7.9 MB

XVIII. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).srt

15 kB

XVIII. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp4

16 MB

XVIII. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).srt

32 kB

XVIII. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4

19 MB

XVIII. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt

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XVIII. Application Example Photo OCR/18 - 4 - Ceiling Analysis What Part of the Pipeline to Work on Next (14 min).mp4

16 MB

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

30 kB

XVIII. Application Example Photo OCR/docs_slides_Lecture18.pdf

2.0 MB

XVIII. Application Example Photo OCR/docs_slides_Lecture18.pptx

6.1 MB