Udemy - Contextual Multi-Armed Bandit Problems in Python

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Udemy - Contextual Multi-Armed Bandit Problems in Python

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收录时间：2025-02-22
最近下载：2025-06-02

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文件列表

  • 5. Contextual Bandit Problems/4. LinUCB Implementation Part 1.mp4 137.5 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/2. Deterministic Environment.mp4 124.7 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/11. Epsilon Greedy Agent.mp4 80.1 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/3. Design of Thompson Sampling Class Part 2.mp4 79.8 MB
  • 2. Introduction to Python/4. Introduction to Python Part 3.mp4 78.8 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/24. Regret Concept and Implementation.mp4 76.6 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/8. Plotting Function Part2.mp4 74.5 MB
  • 5. Contextual Bandit Problems/7. Test LinUCB Algorithm.mp4 72.1 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/15. Create a Stochastic Environment.mp4 71.4 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/7. Plotting Function Part1.mp4 70.9 MB
  • 5. Contextual Bandit Problems/14. Evaluate Agent Performances based on Accumulated Rewards.mp4 69.4 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/6. Results for Random Agent.mp4 63.6 MB
  • 5. Contextual Bandit Problems/13. Test Agents with Accuracy Metric.mp4 63.1 MB
  • 5. Contextual Bandit Problems/3. LinUCB Algorithm Theory.mp4 62.1 MB
  • 5. Contextual Bandit Problems/9. Simulation Functions.mp4 60.2 MB
  • 5. Contextual Bandit Problems/11. Real-world Case Dataset Explanation.mp4 58.9 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/5. Incremental Average Implementation.mp4 58.4 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/10. Greedy Agent.mp4 58.4 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/18. Softmax Agent Implementation.mp4 56.7 MB
  • 1. Introduction/1. Course Overview.mp4 56.3 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/2. Design of Thompson Sampling Class Part 1.mp4 54.2 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/12. Epsilon Greedy Parameter Tuning Part1.mp4 53.2 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/4. Random Agent Class Implementation.mp4 53.1 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/23. Comparisons of All Agent Performance and a Life Lesson.mp4 52.5 MB
  • 5. Contextual Bandit Problems/2. LinUCB Math Notations.mp4 52.4 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/13. Epsilon Greedy Parameter Tuning Part2.mp4 49.2 MB
  • 5. Contextual Bandit Problems/5. LinUCB Implementation Part 2.mp4 48.7 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/1. Environment Design Logic.mp4 48.3 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/1. Why and How We can Use Thompson Sampling.mp4 48.1 MB
  • 1. Introduction/4. Multi-armed Bandit Problems and Their Applications.mp4 48.1 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/21. UCB Algorithm Implementation.mp4 47.3 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/10. Visualization Function for Gaussian Thompson Sampling.mp4 46.3 MB
  • 5. Contextual Bandit Problems/6. LinUCB Implementation Part 3.mp4 44.7 MB
  • 1. Introduction/8.5 ReinforcementLearning_An_Intro.pdf 43.6 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/9. Plot Results for Random Agent.mp4 43.1 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/11. Results for Gaussian Thompson Sampling.mp4 42.5 MB
  • 5. Contextual Bandit Problems/10. Comparison of Epsilon Greedy and LinUCB with Toy Data.mp4 41.0 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/26. Epsilon Greedy with Regret Concept.mp4 40.6 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/25. Regret Function Visualization.mp4 37.9 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/22. UCB Algorithm Results.mp4 37.7 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/6. Theory for Gaussian Thompson Sampling.mp4 36.8 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/3. Proof for Incremental Averaging.mp4 36.2 MB
  • 5. Contextual Bandit Problems/1. Contextual Bandit Problems vs Supervised Learning.mp4 35.5 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/4. Results for Thompson Sampling with Binary Reward.mp4 34.8 MB
  • 5. Contextual Bandit Problems/12. Split Data into Train and Test.mp4 34.0 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/17. Agents Performance with Stochastic Environment.mp4 33.6 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/16. Create an Instance of Stochastic Environment.mp4 31.6 MB
  • 1. Introduction/6. Similarities and Differences between Bandit Problems and Reinforcement Learning.mp4 31.0 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/9. Parameter Update Module for Gaussian Thompson Sampling Agent.mp4 30.9 MB
  • 5. Contextual Bandit Problems/8. Epsilon Greedy Algorithm Implementation.mp4 30.8 MB
  • 2. Introduction to Python/2. Introduction to Python Part 1.mp4 30.8 MB
  • 2. Introduction to Python/1. Introduction to Google Colab.mp4 29.3 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/8. Select Arm Module for Gaussian Thompson Sampling Class.mp4 27.8 MB
  • 2. Introduction to Python/3. Introduction to Python Part 2.mp4 25.7 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/5. Thompson Sampling For Binary Reward with Stochastic Environment.mp4 24.9 MB
  • 1. Introduction/5. Multi-armed Bandit Problems for Startup Founders.mp4 24.7 MB
  • 1. Introduction/2. Casino and Statistics.mp4 24.7 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/27. Regret Curves Results for Deterministic Environment.mp4 23.4 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/28. Regret Curves Results for Stochastic Environment.mp4 22.5 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/20. Upper Confidence Bound (UCB) Algorithm Theory.mp4 21.8 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/19. Softmax Agent Results.mp4 21.4 MB
  • 4. Thompson Sampling for Multi-Armed Bandits/7. Environment for Gaussian Thompson Sampling.mp4 20.6 MB
  • 1. Introduction/3. Story A Gambler in Casino.mp4 19.9 MB
  • 3. Fundamental Algorithms in Multi-Armed Bandits Problems/14. Difference Between Stochasticity, Uncertainty, and Non-Stationary.mp4 18.5 MB
  • 1. Introduction/8.4 BanditAlgorithms.pdf 5.3 MB
  • 1. Introduction/7.2 02-Introduction.pptx 3.4 MB
  • 1. Introduction/8.3 A Tutorial on Thompson Sampling.pdf 3.3 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 1.2 MB
  • 5. Contextual Bandit Problems/2.1 LinUCB_Notations.pdf 601.0 kB
  • 1. Introduction/7.1 01-Course Overview.pptx 400.7 kB
  • 1. Introduction/8.2 A Contextual Bandit for news.pdf 306.1 kB
  • 5. Contextual Bandit Problems/15.3 data_cleaning.ipynb 258.7 kB
  • 5. Contextual Bandit Problems/15.1 balanced_data_short.csv 213.1 kB
  • 5. Contextual Bandit Problems/15.2 balanced_data.csv 175.8 kB
  • 4. Thompson Sampling for Multi-Armed Bandits/12.1 MAB_Thompson_Sampling.ipynb 168.8 kB
  • 5. Contextual Bandit Problems/16.1 MAB_Contextual_BP.ipynb 139.4 kB
  • 2. Introduction to Python/5.1 MAB_Udemy_Course_introduction_python.ipynb 8.1 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

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