Udemy - Contextual Multi-Armed Bandit Problems in Python

Creation Time	March 6, 2025, 2:23 p.m.
Last Access Time	May 2, 2025, 11:53 p.m.
File Size	2.9 GB
Keywords	Python - Problems Udemy Bandit in Multi-Armed Contextual
Total Requests	126
Total Files	89
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5. Contextual Bandit Problems/4. LinUCB Implementation Part 1.mp4 131.1 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/2. Deterministic Environment.mp4 118.9 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/11. Epsilon Greedy Agent.mp4 76.4 MB
4. Thompson Sampling for Multi-Armed Bandits/3. Design of Thompson Sampling Class Part 2.mp4 76.1 MB
2. Introduction to Python/4. Introduction to Python Part 3.mp4 75.2 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/24. Regret Concept and Implementation.mp4 73.1 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/8. Plotting Function Part2.mp4 71.0 MB
5. Contextual Bandit Problems/7. Test LinUCB Algorithm.mp4 68.8 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/15. Create a Stochastic Environment.mp4 68.1 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/7. Plotting Function Part1.mp4 67.6 MB
5. Contextual Bandit Problems/14. Evaluate Agent Performances based on Accumulated Rewards.mp4 66.2 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/6. Results for Random Agent.mp4 60.7 MB
5. Contextual Bandit Problems/13. Test Agents with Accuracy Metric.mp4 60.2 MB
5. Contextual Bandit Problems/3. LinUCB Algorithm Theory.mp4 59.3 MB
5. Contextual Bandit Problems/9. Simulation Functions.mp4 57.4 MB
5. Contextual Bandit Problems/11. Real-world Case Dataset Explanation.mp4 56.2 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/5. Incremental Average Implementation.mp4 55.7 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/10. Greedy Agent.mp4 55.7 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/18. Softmax Agent Implementation.mp4 54.0 MB
1. Introduction/1. Course Overview.mp4 53.7 MB
4. Thompson Sampling for Multi-Armed Bandits/2. Design of Thompson Sampling Class Part 1.mp4 51.7 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/12. Epsilon Greedy Parameter Tuning Part1.mp4 50.8 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/4. Random Agent Class Implementation.mp4 50.6 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/23. Comparisons of All Agent Performance and a Life Lesson.mp4 50.1 MB
5. Contextual Bandit Problems/2. LinUCB Math Notations.mp4 49.9 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/13. Epsilon Greedy Parameter Tuning Part2.mp4 46.9 MB
5. Contextual Bandit Problems/5. LinUCB Implementation Part 2.mp4 46.4 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/1. Environment Design Logic.mp4 46.0 MB
4. Thompson Sampling for Multi-Armed Bandits/1. Why and How We can Use Thompson Sampling.mp4 45.8 MB
1. Introduction/4. Multi-armed Bandit Problems and Their Applications.mp4 45.8 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/21. UCB Algorithm Implementation.mp4 45.2 MB
4. Thompson Sampling for Multi-Armed Bandits/10. Visualization Function for Gaussian Thompson Sampling.mp4 44.2 MB
5. Contextual Bandit Problems/6. LinUCB Implementation Part 3.mp4 42.6 MB
1. Introduction/8.5 ReinforcementLearning_An_Intro.pdf 41.6 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/9. Plot Results for Random Agent.mp4 41.1 MB
4. Thompson Sampling for Multi-Armed Bandits/11. Results for Gaussian Thompson Sampling.mp4 40.5 MB
5. Contextual Bandit Problems/10. Comparison of Epsilon Greedy and LinUCB with Toy Data.mp4 39.1 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/26. Epsilon Greedy with Regret Concept.mp4 38.8 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/25. Regret Function Visualization.mp4 36.1 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/22. UCB Algorithm Results.mp4 36.0 MB
4. Thompson Sampling for Multi-Armed Bandits/6. Theory for Gaussian Thompson Sampling.mp4 35.1 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/3. Proof for Incremental Averaging.mp4 34.5 MB
5. Contextual Bandit Problems/1. Contextual Bandit Problems vs Supervised Learning.mp4 33.8 MB
4. Thompson Sampling for Multi-Armed Bandits/4. Results for Thompson Sampling with Binary Reward.mp4 33.2 MB
5. Contextual Bandit Problems/12. Split Data into Train and Test.mp4 32.4 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/17. Agents Performance with Stochastic Environment.mp4 32.1 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/16. Create an Instance of Stochastic Environment.mp4 30.1 MB
1. Introduction/6. Similarities and Differences between Bandit Problems and Reinforcement Learning.mp4 29.6 MB
4. Thompson Sampling for Multi-Armed Bandits/9. Parameter Update Module for Gaussian Thompson Sampling Agent.mp4 29.4 MB
5. Contextual Bandit Problems/8. Epsilon Greedy Algorithm Implementation.mp4 29.4 MB
2. Introduction to Python/2. Introduction to Python Part 1.mp4 29.4 MB
2. Introduction to Python/1. Introduction to Google Colab.mp4 27.9 MB
4. Thompson Sampling for Multi-Armed Bandits/8. Select Arm Module for Gaussian Thompson Sampling Class.mp4 26.5 MB
2. Introduction to Python/3. Introduction to Python Part 2.mp4 24.5 MB
4. Thompson Sampling for Multi-Armed Bandits/5. Thompson Sampling For Binary Reward with Stochastic Environment.mp4 23.8 MB
1. Introduction/5. Multi-armed Bandit Problems for Startup Founders.mp4 23.6 MB
1. Introduction/2. Casino and Statistics.mp4 23.6 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/27. Regret Curves Results for Deterministic Environment.mp4 22.3 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/28. Regret Curves Results for Stochastic Environment.mp4 21.5 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/20. Upper Confidence Bound (UCB) Algorithm Theory.mp4 20.8 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/19. Softmax Agent Results.mp4 20.4 MB
4. Thompson Sampling for Multi-Armed Bandits/7. Environment for Gaussian Thompson Sampling.mp4 19.7 MB
1. Introduction/3. Story A Gambler in Casino.mp4 18.9 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/14. Difference Between Stochasticity, Uncertainty, and Non-Stationary.mp4 17.6 MB
1. Introduction/8.4 BanditAlgorithms.pdf 5.1 MB
1. Introduction/7.2 02-Introduction.pptx 3.3 MB
1. Introduction/8.3 A Tutorial on Thompson Sampling.pdf 3.1 MB
1. Introduction/8.1 A Contextual Bandit Bake-off.pdf 1.2 MB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/29.1 MAB_Udemy_Basic_Agents.ipynb 1.1 MB
5. Contextual Bandit Problems/2.1 LinUCB_Notations.pdf 586.9 KB
1. Introduction/7.1 01-Course Overview.pptx 391.4 KB
1. Introduction/8.2 A Contextual Bandit for news.pdf 298.9 KB
5. Contextual Bandit Problems/15.3 data_cleaning.ipynb 252.6 KB
5. Contextual Bandit Problems/15.1 balanced_data_short.csv 208.1 KB
5. Contextual Bandit Problems/15.2 balanced_data.csv 171.7 KB
4. Thompson Sampling for Multi-Armed Bandits/12.1 MAB_Thompson_Sampling.ipynb 164.9 KB
5. Contextual Bandit Problems/16.1 MAB_Contextual_BP.ipynb 136.1 KB
2. Introduction to Python/5.1 MAB_Udemy_Course_introduction_python.ipynb 7.9 KB
1. Introduction/8. Resources.html 165 bytes
5. Contextual Bandit Problems/15. Datasets and Data Preparation Code.html 150 bytes
1. Introduction/9. The most important difference between RL and MAB.html 147 bytes
3. Fundamental Algorithms in Multi-Armed Bandits Problems/30. Regret Concept.html 147 bytes
4. Thompson Sampling for Multi-Armed Bandits/13. Questions.html 147 bytes
5. Contextual Bandit Problems/17. Concept of LinUCB algorithm.html 147 bytes
4. Thompson Sampling for Multi-Armed Bandits/12. Code for Thompson Sampling.html 101 bytes
1. Introduction/7. Slides.html 79 bytes
2. Introduction to Python/5. Code for Introduction to Python.html 74 bytes
5. Contextual Bandit Problems/16. Code for Contextual Bandit Problems.html 67 bytes
3. Fundamental Algorithms in Multi-Armed Bandits Problems/29. Code for Basic Agents.html 38 bytes

Udemy - Contextual Multi-Armed Bandit Problems in Python

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