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585б |
1. An Easy Introduction to K-Means Clustering.mp4 |
12.55Мб |
1. An Easy Introduction to K-Means Clustering.srt |
9.44Кб |
1. Gaussian Mixture Model (GMM) Algorithm.mp4 |
65.78Мб |
1. Gaussian Mixture Model (GMM) Algorithm.srt |
20.16Кб |
1. How to Code by Yourself (part 1).mp4 |
24.53Мб |
1. How to Code by Yourself (part 1).srt |
22.75Кб |
1. How to Succeed in this Course (Long Version).mp4 |
18.31Мб |
1. How to Succeed in this Course (Long Version).srt |
14.55Кб |
1. Introduction.mp4 |
45.64Мб |
1. Introduction.srt |
6.89Кб |
1. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4 |
4.40Мб |
1. Visual Walkthrough of Agglomerative Hierarchical Clustering.srt |
3.51Кб |
1. What is the Appendix.mp4 |
5.45Мб |
1. What is the Appendix.srt |
3.72Кб |
1. Windows-Focused Environment Setup 2018.mp4 |
186.30Мб |
1. Windows-Focused Environment Setup 2018.srt |
20.10Кб |
10. Expectation-Maximization (pt 3).mp4 |
31.29Мб |
10. Expectation-Maximization (pt 3).srt |
10.06Кб |
10. Soft K-Means.mp4 |
25.25Мб |
10. Soft K-Means.srt |
6.95Кб |
11. Future Unsupervised Learning Algorithms You Will Learn.mp4 |
1.95Мб |
11. Future Unsupervised Learning Algorithms You Will Learn.srt |
1.40Кб |
11. The Soft K-Means Objective Function.mp4 |
3.02Мб |
11. The Soft K-Means Objective Function.srt |
2.05Кб |
12. Soft K-Means in Python Code.mp4 |
30.21Мб |
12. Soft K-Means in Python Code.srt |
7.83Кб |
13. How to Pace Yourself.mp4 |
22.39Мб |
13. How to Pace Yourself.srt |
4.71Кб |
14. Visualizing Each Step of K-Means.mp4 |
5.25Мб |
14. Visualizing Each Step of K-Means.srt |
2.68Кб |
15. Examples of where K-Means can fail.mp4 |
17.00Мб |
15. Examples of where K-Means can fail.srt |
5.20Кб |
16. Disadvantages of K-Means Clustering.mp4 |
3.88Мб |
16. Disadvantages of K-Means Clustering.srt |
3.29Кб |
17. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4 |
11.39Мб |
17. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).srt |
9.02Кб |
18. Using K-Means on Real Data MNIST.mp4 |
10.70Мб |
18. Using K-Means on Real Data MNIST.srt |
6.98Кб |
19. One Way to Choose K.mp4 |
9.07Мб |
19. One Way to Choose K.srt |
5.07Кб |
2. Agglomerative Clustering Options.mp4 |
6.22Мб |
2. Agglomerative Clustering Options.srt |
5.43Кб |
2. BONUS Where to get discount coupons and FREE deep learning material.mp4 |
37.80Мб |
2. BONUS Where to get discount coupons and FREE deep learning material.srt |
7.87Кб |
2. Course Outline.mp4 |
20.26Мб |
2. Course Outline.srt |
6.00Кб |
2. Hard K-Means Exercise Prompt 1.mp4 |
50.03Мб |
2. Hard K-Means Exercise Prompt 1.srt |
11.53Кб |
2. How to Code by Yourself (part 2).mp4 |
14.80Мб |
2. How to Code by Yourself (part 2).srt |
13.26Кб |
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
43.92Мб |
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt |
14.48Кб |
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
38.95Мб |
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt |
31.79Кб |
2. Write a Gaussian Mixture Model in Python Code.mp4 |
137.47Мб |
2. Write a Gaussian Mixture Model in Python Code.srt |
24.90Кб |
20. K-Means Application Finding Clusters of Related Words.mp4 |
25.98Мб |
20. K-Means Application Finding Clusters of Related Words.srt |
8.39Кб |
21. Clustering for NLP and Computer Vision Real-World Applications.mp4 |
42.41Мб |
21. Clustering for NLP and Computer Vision Real-World Applications.srt |
9.15Кб |
22. Suggestion Box.mp4 |
16.10Мб |
22. Suggestion Box.srt |
4.70Кб |
3. Hard K-Means Exercise 1 Solution.mp4 |
55.43Мб |
3. Hard K-Means Exercise 1 Solution.srt |
13.82Кб |
3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
29.32Мб |
3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt |
16.03Кб |
3. Practical Issues with GMM Singular Covariance.mp4 |
43.32Мб |
3. Practical Issues with GMM Singular Covariance.srt |
12.11Кб |
3. Proof that using Jupyter Notebook is the same as not using it.mp4 |
78.28Мб |
3. Proof that using Jupyter Notebook is the same as not using it.srt |
14.12Кб |
3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4 |
11.85Мб |
3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.srt |
4.39Кб |
3. What is unsupervised learning used for.mp4 |
29.06Мб |
3. What is unsupervised learning used for.srt |
7.18Кб |
4. Application Evolution.mp4 |
26.39Мб |
4. Application Evolution.srt |
16.22Кб |
4. Comparison between GMM and K-Means.mp4 |
19.15Мб |
4. Comparison between GMM and K-Means.srt |
4.99Кб |
4. Hard K-Means Exercise Prompt 2.mp4 |
22.99Мб |
4. Hard K-Means Exercise Prompt 2.srt |
6.13Кб |
4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
37.62Мб |
4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt |
23.04Кб |
4. Python 2 vs Python 3.mp4 |
7.83Мб |
4. Python 2 vs Python 3.srt |
6.10Кб |
4. Why Use Clustering.mp4 |
54.85Мб |
4. Why Use Clustering.srt |
12.14Кб |
5.1 Github Link.html |
120б |
5. Application Donald Trump vs. Hillary Clinton Tweets.mp4 |
35.27Мб |
5. Application Donald Trump vs. Hillary Clinton Tweets.srt |
19.41Кб |
5. Hard K-Means Exercise 2 Solution.mp4 |
33.33Мб |
5. Hard K-Means Exercise 2 Solution.srt |
8.44Кб |
5. Kernel Density Estimation.mp4 |
29.95Мб |
5. Kernel Density Estimation.srt |
8.41Кб |
5. Where to get the code.mp4 |
23.06Мб |
5. Where to get the code.srt |
6.26Кб |
6. Anyone Can Succeed in this Course.mp4 |
78.06Мб |
6. Anyone Can Succeed in this Course.srt |
17.10Кб |
6. GMM vs Bayes Classifier (pt 1).mp4 |
41.31Мб |
6. GMM vs Bayes Classifier (pt 1).srt |
12.50Кб |
6. Hard K-Means Exercise Prompt 3.mp4 |
41.84Мб |
6. Hard K-Means Exercise Prompt 3.srt |
8.70Кб |
7. GMM vs Bayes Classifier (pt 2).mp4 |
45.19Мб |
7. GMM vs Bayes Classifier (pt 2).srt |
14.64Кб |
7. Hard K-Means Exercise 3 Solution.mp4 |
91.35Мб |
7. Hard K-Means Exercise 3 Solution.srt |
20.51Кб |
8. Expectation-Maximization (pt 1).mp4 |
49.81Мб |
8. Expectation-Maximization (pt 1).srt |
14.92Кб |
8. Hard K-Means Objective Theory.mp4 |
51.92Мб |
8. Hard K-Means Objective Theory.srt |
16.95Кб |
9. Expectation-Maximization (pt 2).mp4 |
10.87Мб |
9. Expectation-Maximization (pt 2).srt |
2.62Кб |
9. Hard K-Means Objective Code.mp4 |
27.69Мб |
9. Hard K-Means Objective Code.srt |
5.99Кб |
TutsNode.com.txt |
63б |