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Название Cluster Analysis and Unsupervised Machine Learning in Python
Тип Порно
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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
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