Udemy Contextual Multi Armed Bandit Problems in Python | D9F80CEE680EE8EE6C8224A08DEAA9DEE94593CD

Udemy Contextual Multi Armed Bandit Problems in Python

Name	Udemy - Contextual Multi-Armed Bandit Problems in Python	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	3.1 GB
Total Files	89
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Hash	D9F80CEE680EE8EE6C8224A08DEAA9DEE94593CD

/5. Contextual Bandit Problems/
4. LinUCB Implementation Part 1.mp4	137.5 MB
1. Introduction/
2. Casino and Statistics.mp4	24.7 MB
3. Story A Gambler in Casino.mp4	19.9 MB
4. Multi-armed Bandit Problems and Their Applications.mp4	48.1 MB
5. Multi-armed Bandit Problems for Startup Founders.mp4	24.7 MB
6. Similarities and Differences between Bandit Problems and Reinforcement Learning.mp4	31.0 MB
7. Slides.html	0.1 KB
7.1 01-Course Overview.pptx	400.7 KB
7.2 02-Introduction.pptx	3.4 MB
8. Resources.html	0.2 KB
8.1 A Contextual Bandit Bake-off.pdf	1.2 MB
8.2 A Contextual Bandit for news.pdf	306.1 KB
8.3 A Tutorial on Thompson Sampling.pdf	3.3 MB
8.4 BanditAlgorithms.pdf	5.3 MB
8.5 ReinforcementLearning_An_Intro.pdf	43.6 MB
9. The most important difference between RL and MAB.html	0.1 KB
1. Course Overview.mp4	56.3 MB
2. Introduction to Python/
1. Introduction to Google Colab.mp4	29.3 MB
2. Introduction to Python Part 1.mp4	30.8 MB
3. Introduction to Python Part 2.mp4	25.7 MB
4. Introduction to Python Part 3.mp4	78.8 MB
5. Code for Introduction to Python.html	0.1 KB
5.1 MAB_Udemy_Course_introduction_python.ipynb	8.1 KB
3. Fundamental Algorithms in Multi-Armed Bandits Problems/
1. Environment Design Logic.mp4	48.3 MB
2. Deterministic Environment.mp4	124.7 MB
3. Proof for Incremental Averaging.mp4	36.2 MB
4. Random Agent Class Implementation.mp4	53.1 MB
5. Incremental Average Implementation.mp4	58.4 MB
6. Results for Random Agent.mp4	63.6 MB
7. Plotting Function Part1.mp4	70.9 MB
8. Plotting Function Part2.mp4	74.5 MB
9. Plot Results for Random Agent.mp4	43.1 MB
10. Greedy Agent.mp4	58.4 MB
11. Epsilon Greedy Agent.mp4	80.1 MB
12. Epsilon Greedy Parameter Tuning Part1.mp4	53.2 MB
13. Epsilon Greedy Parameter Tuning Part2.mp4	49.2 MB
14. Difference Between Stochasticity, Uncertainty, and Non-Stationary.mp4	18.5 MB
15. Create a Stochastic Environment.mp4	71.4 MB
16. Create an Instance of Stochastic Environment.mp4	31.6 MB
17. Agents Performance with Stochastic Environment.mp4	33.6 MB
18. Softmax Agent Implementation.mp4	56.7 MB
19. Softmax Agent Results.mp4	21.4 MB
20. Upper Confidence Bound (UCB) Algorithm Theory.mp4	21.8 MB
21. UCB Algorithm Implementation.mp4	47.3 MB
22. UCB Algorithm Results.mp4	37.7 MB
23. Comparisons of All Agent Performance and a Life Lesson.mp4	52.5 MB
24. Regret Concept and Implementation.mp4	76.6 MB
25. Regret Function Visualization.mp4	37.9 MB
26. Epsilon Greedy with Regret Concept.mp4	40.6 MB
27. Regret Curves Results for Deterministic Environment.mp4	23.4 MB
28. Regret Curves Results for Stochastic Environment.mp4	22.5 MB
29. Code for Basic Agents.html	0.0 KB
29.1 MAB_Udemy_Basic_Agents.ipynb	1.2 MB
30. Regret Concept.html	0.1 KB
4. Thompson Sampling for Multi-Armed Bandits/
1. Why and How We can Use Thompson Sampling.mp4	48.1 MB
2. Design of Thompson Sampling Class Part 1.mp4	54.2 MB
3. Design of Thompson Sampling Class Part 2.mp4	79.8 MB
4. Results for Thompson Sampling with Binary Reward.mp4	34.8 MB
5. Thompson Sampling For Binary Reward with Stochastic Environment.mp4	24.9 MB
6. Theory for Gaussian Thompson Sampling.mp4	36.8 MB
7. Environment for Gaussian Thompson Sampling.mp4	20.6 MB
8. Select Arm Module for Gaussian Thompson Sampling Class.mp4	27.8 MB
9. Parameter Update Module for Gaussian Thompson Sampling Agent.mp4	30.9 MB
10. Visualization Function for Gaussian Thompson Sampling.mp4	46.3 MB
11. Results for Gaussian Thompson Sampling.mp4	42.5 MB
12. Code for Thompson Sampling.html	0.1 KB
12.1 MAB_Thompson_Sampling.ipynb	168.8 KB
13. Questions.html	0.1 KB
5. Contextual Bandit Problems/
1. Contextual Bandit Problems vs Supervised Learning.mp4	35.5 MB
2. LinUCB Math Notations.mp4	52.4 MB
2.1 LinUCB_Notations.pdf	601.0 KB
3. LinUCB Algorithm Theory.mp4	62.1 MB
5. LinUCB Implementation Part 2.mp4	48.7 MB
6. LinUCB Implementation Part 3.mp4	44.7 MB
7. Test LinUCB Algorithm.mp4	72.1 MB
8. Epsilon Greedy Algorithm Implementation.mp4	30.8 MB
9. Simulation Functions.mp4	60.2 MB
10. Comparison of Epsilon Greedy and LinUCB with Toy Data.mp4	41.0 MB
11. Real-world Case Dataset Explanation.mp4	58.9 MB
12. Split Data into Train and Test.mp4	34.0 MB
13. Test Agents with Accuracy Metric.mp4	63.1 MB
14. Evaluate Agent Performances based on Accumulated Rewards.mp4	69.4 MB
15. Datasets and Data Preparation Code.html	0.2 KB
15.1 balanced_data_short.csv	213.1 KB
15.2 balanced_data.csv	175.8 KB
15.3 data_cleaning.ipynb	258.7 KB
16. Code for Contextual Bandit Problems.html	0.1 KB
16.1 MAB_Contextual_BP.ipynb	139.4 KB
17. Concept of LinUCB algorithm.html	0.1 KB
Total files 89