[FreeCourseSite.com] Udemy - Artificial Intelligence - Reinforcement Learning in Python

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[FreeCourseSite.com] Udemy - Artificial Intelligence - Reinforcement Learning in Python

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种子哈希：edcbe0cda27fd80b9be3be832b7148dd69c01a97
文件大小： 4.14G
已经下载：1387次
下载速度：极快
收录时间：2022-01-20
最近下载：2024-12-19

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文件列表

  • 11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 195.4 MB
  • 4. Markov Decision Proccesses/11. Bellman Examples.mp4 91.3 MB
  • 10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead.mp4 87.8 MB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 8. Approximation Methods/7. Approximation Methods for Control Code.mp4 81.5 MB
  • 2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 78.1 MB
  • 5. Dynamic Programming/5. Iterative Policy Evaluation in Code.mp4 71.8 MB
  • 10. Stock Trading Project with Reinforcement Learning/7. Code pt 2.mp4 68.5 MB
  • 6. Monte Carlo/5. Monte Carlo Control in Code.mp4 67.5 MB
  • 1. Welcome/5. Warmup.mp4 65.6 MB
  • 8. Approximation Methods/5. Approximation Methods for Prediction Code.mp4 65.3 MB
  • 4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs).mp4 64.7 MB
  • 5. Dynamic Programming/2. Iterative Policy Evaluation.mp4 63.8 MB
  • 5. Dynamic Programming/10. Policy Iteration in Code.mp4 59.1 MB
  • 4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1).mp4 58.8 MB
  • 2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 58.6 MB
  • 2. Return of the Multi-Armed Bandit/12. UCB1 Theory.mp4 58.2 MB
  • 3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp4 57.3 MB
  • 4. Markov Decision Proccesses/2. Gridworld.mp4 56.6 MB
  • 10. Stock Trading Project with Reinforcement Learning/9. Code pt 4.mp4 55.5 MB
  • 10. Stock Trading Project with Reinforcement Learning/3. Data and Environment.mp4 54.5 MB
  • 2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma.mp4 54.5 MB
  • 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 54.2 MB
  • 5. Dynamic Programming/11. Policy Iteration in Windy Gridworld.mp4 53.9 MB
  • 2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4 53.7 MB
  • 2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs.mp4 52.8 MB
  • 10. Stock Trading Project with Reinforcement Learning/6. Code pt 1.mp4 52.1 MB
  • 2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory.mp4 50.9 MB
  • 6. Monte Carlo/1. Monte Carlo Intro.mp4 49.9 MB
  • 6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 49.4 MB
  • 5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code.mp4 49.2 MB
  • 8. Approximation Methods/9. CartPole Code.mp4 49.1 MB
  • 5. Dynamic Programming/4. Gridworld in Code.mp4 49.1 MB
  • 8. Approximation Methods/3. Feature Engineering.mp4 48.1 MB
  • 5. Dynamic Programming/13. Value Iteration in Code.mp4 47.9 MB
  • 7. Temporal Difference Learning/5. SARSA in Code.mp4 47.1 MB
  • 10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning.mp4 47.1 MB
  • 5. Dynamic Programming/8. Policy Improvement.mp4 46.1 MB
  • 11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
  • 1. Welcome/4. How to Succeed in this Course.mp4 46.0 MB
  • 2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons.mp4 45.8 MB
  • 2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code.mp4 45.5 MB
  • 5. Dynamic Programming/6. Windy Gridworld in Code.mp4 43.5 MB
  • 2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code.mp4 43.4 MB
  • 3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning.mp4 43.2 MB
  • 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code.mp4 42.7 MB
  • 1. Welcome/2. Course Outline and Big Picture.mp4 41.6 MB
  • 4. Markov Decision Proccesses/6. Future Rewards.mp4 41.4 MB
  • 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
  • 7. Temporal Difference Learning/7. Q Learning in Code.mp4 40.4 MB
  • 9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference.mp4 40.1 MB
  • 14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
  • 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
  • 4. Markov Decision Proccesses/1. MDP Section Introduction.mp4 39.0 MB
  • 6. Monte Carlo/4. Monte Carlo Control.mp4 37.3 MB
  • 5. Dynamic Programming/12. Value Iteration.mp4 37.0 MB
  • 5. Dynamic Programming/1. Dynamic Programming Section Introduction.mp4 36.4 MB
  • 2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning.mp4 36.3 MB
  • 8. Approximation Methods/4. Approximation Methods for Prediction.mp4 36.0 MB
  • 1. Welcome/1. Introduction.mp4 35.9 MB
  • 5. Dynamic Programming/9. Policy Iteration.mp4 35.8 MB
  • 10. Stock Trading Project with Reinforcement Learning/8. Code pt 3.mp4 35.4 MB
  • 2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code.mp4 34.4 MB
  • 4. Markov Decision Proccesses/3. Choosing Rewards.mp4 34.1 MB
  • 7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 34.0 MB
  • 8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 32.6 MB
  • 2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits.mp4 32.5 MB
  • 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
  • 2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt.mp4 30.1 MB
  • 2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory.mp4 29.7 MB
  • 4. Markov Decision Proccesses/8. The Bellman Equation (pt 1).mp4 29.1 MB
  • 2. Return of the Multi-Armed Bandit/21. Why don't we just use a library.mp4 28.7 MB
  • 8. Approximation Methods/8. CartPole.mp4 28.2 MB
  • 10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction.mp4 28.1 MB
  • 4. Markov Decision Proccesses/9. The Bellman Equation (pt 2).mp4 28.0 MB
  • 5. Dynamic Programming/14. Dynamic Programming Summary.mp4 26.3 MB
  • 4. Markov Decision Proccesses/10. The Bellman Equation (pt 3).mp4 25.9 MB
  • 2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code.mp4 25.8 MB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25.7 MB
  • 2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program.mp4 25.7 MB
  • 2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory.mp4 24.7 MB
  • 6. Monte Carlo/6. Monte Carlo Control without Exploring Starts.mp4 24.5 MB
  • 10. Stock Trading Project with Reinforcement Learning/5. Design of the Program.mp4 24.4 MB
  • 2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1).mp4 24.3 MB
  • 1. Welcome/3. Where to get the Code.mp4 23.8 MB
  • 5. Dynamic Programming/3. Designing Your RL Program.mp4 23.4 MB
  • 8. Approximation Methods/1. Approximation Methods Section Introduction.mp4 23.1 MB
  • 4. Markov Decision Proccesses/4. The Markov Property.mp4 22.8 MB
  • 8. Approximation Methods/11. Approximation Methods Section Summary.mp4 22.8 MB
  • 2. Return of the Multi-Armed Bandit/14. UCB1 Code.mp4 21.7 MB
  • 7. Temporal Difference Learning/6. Q Learning.mp4 20.8 MB
  • 4. Markov Decision Proccesses/7. Value Functions.mp4 19.5 MB
  • 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
  • 2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt.mp4 18.8 MB
  • 8. Approximation Methods/6. Approximation Methods for Control.mp4 18.4 MB
  • 8. Approximation Methods/10. Approximation Methods Exercise.mp4 18.4 MB
  • 7. Temporal Difference Learning/4. SARSA.mp4 17.0 MB
  • 2. Return of the Multi-Armed Bandit/25. Suggestion Box.mp4 16.9 MB
  • 7. Temporal Difference Learning/2. TD(0) Prediction.mp4 16.6 MB
  • 10. Stock Trading Project with Reinforcement Learning/10. Stock Trading Project Discussion.mp4 16.5 MB
  • 4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2).mp4 16.5 MB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp4 15.5 MB
  • 7. Temporal Difference Learning/1. Temporal Difference Introduction.mp4 15.1 MB
  • 4. Markov Decision Proccesses/14. MDP Summary.mp4 15.0 MB
  • 2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt.mp4 14.4 MB
  • 2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt.mp4 13.4 MB
  • 6. Monte Carlo/8. Monte Carlo Summary.mp4 12.0 MB
  • 7. Temporal Difference Learning/8. TD Learning Section Summary.mp4 10.5 MB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp4 8.2 MB
  • 14. Appendix FAQ Finale/1. What is the Appendix.mp4 5.7 MB
  • 4. Markov Decision Proccesses/11. Bellman Examples-en_US.srt 27.3 kB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1)-en_US.srt 26.6 kB
  • 2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srt 23.3 kB
  • 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt 22.7 kB
  • 5. Dynamic Programming/2. Iterative Policy Evaluation-en_US.srt 20.9 kB
  • 10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead-en_US.srt 20.4 kB
  • 11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018-en_US.srt 19.8 kB
  • 2. Return of the Multi-Armed Bandit/12. UCB1 Theory-en_US.srt 19.6 kB
  • 4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs)-en_US.srt 19.3 kB
  • 1. Welcome/5. Warmup-en_US.srt 18.6 kB
  • 4. Markov Decision Proccesses/2. Gridworld-en_US.srt 17.0 kB
  • 2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srt 16.5 kB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2)-en_US.srt 16.2 kB
  • 5. Dynamic Programming/4. Gridworld in Code-en_US.srt 16.1 kB
  • 11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt 16.1 kB
  • 5. Dynamic Programming/5. Iterative Policy Evaluation in Code-en_US.srt 16.0 kB
  • 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt 15.8 kB
  • 10. Stock Trading Project with Reinforcement Learning/3. Data and Environment-en_US.srt 15.5 kB
  • 2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory-en_US.srt 14.8 kB
  • 5. Dynamic Programming/8. Policy Improvement-en_US.srt 14.5 kB
  • 6. Monte Carlo/2. Monte Carlo Policy Evaluation-en_US.srt 14.4 kB
  • 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version)-en_US.srt 14.3 kB
  • 2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs-en_US.srt 14.3 kB
  • 8. Approximation Methods/3. Feature Engineering-en_US.srt 14.2 kB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it-en_US.srt 13.9 kB
  • 2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma-en_US.srt 13.3 kB
  • 4. Markov Decision Proccesses/6. Future Rewards-en_US.srt 12.5 kB
  • 6. Monte Carlo/1. Monte Carlo Intro-en_US.srt 12.4 kB
  • 8. Approximation Methods/4. Approximation Methods for Prediction-en_US.srt 12.3 kB
  • 5. Dynamic Programming/1. Dynamic Programming Section Introduction-en_US.srt 12.2 kB
  • 3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning-en_US.srt 11.9 kB
  • 10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning-en_US.srt 11.9 kB
  • 10. Stock Trading Project with Reinforcement Learning/7. Code pt 2-en_US.srt 11.6 kB
  • 6. Monte Carlo/4. Monte Carlo Control-en_US.srt 11.4 kB
  • 4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srt 11.3 kB
  • 8. Approximation Methods/2. Linear Models for Reinforcement Learning-en_US.srt 11.2 kB
  • 6. Monte Carlo/5. Monte Carlo Control in Code-en_US.srt 11.0 kB
  • 4. Markov Decision Proccesses/8. The Bellman Equation (pt 1)-en_US.srt 10.9 kB
  • 5. Dynamic Programming/11. Policy Iteration in Windy Gridworld-en_US.srt 10.8 kB
  • 8. Approximation Methods/7. Approximation Methods for Control Code-en_US.srt 10.8 kB
  • 3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning-en_US.srt 10.8 kB
  • 2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma-en_US.srt 10.8 kB
  • 5. Dynamic Programming/10. Policy Iteration in Code-en_US.srt 10.6 kB
  • 8. Approximation Methods/5. Approximation Methods for Prediction Code-en_US.srt 10.4 kB
  • 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code-en_US.srt 10.4 kB
  • 1. Welcome/2. Course Outline and Big Picture-en_US.srt 10.3 kB
  • 5. Dynamic Programming/6. Windy Gridworld in Code-en_US.srt 10.3 kB
  • 5. Dynamic Programming/9. Policy Iteration-en_US.srt 10.2 kB
  • 9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference-en_US.srt 10.1 kB
  • 5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code-en_US.srt 9.6 kB
  • 5. Dynamic Programming/12. Value Iteration-en_US.srt 9.5 kB
  • 10. Stock Trading Project with Reinforcement Learning/6. Code pt 1-en_US.srt 9.5 kB
  • 2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits-en_US.srt 9.4 kB
  • 2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory-en_US.srt 9.3 kB
  • 2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning-en_US.srt 9.0 kB
  • 5. Dynamic Programming/13. Value Iteration in Code-en_US.srt 8.7 kB
  • 2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code-en_US.srt 8.5 kB
  • 4. Markov Decision Proccesses/9. The Bellman Equation (pt 2)-en_US.srt 8.4 kB
  • 10. Stock Trading Project with Reinforcement Learning/5. Design of the Program-en_US.srt 8.4 kB
  • 4. Markov Decision Proccesses/1. MDP Section Introduction-en_US.srt 8.2 kB
  • 1. Welcome/4. How to Succeed in this Course-en_US.srt 8.1 kB
  • 10. Stock Trading Project with Reinforcement Learning/9. Code pt 4-en_US.srt 8.1 kB
  • 4. Markov Decision Proccesses/4. The Markov Property-en_US.srt 7.9 kB
  • 14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material-en_US.srt 7.8 kB
  • 7. Temporal Difference Learning/5. SARSA in Code-en_US.srt 7.6 kB
  • 4. Markov Decision Proccesses/10. The Bellman Equation (pt 3)-en_US.srt 7.6 kB
  • 2. Return of the Multi-Armed Bandit/21. Why don't we just use a library-en_US.srt 7.5 kB
  • 2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1)-en_US.srt 7.4 kB
  • 8. Approximation Methods/8. CartPole-en_US.srt 7.2 kB
  • 2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code-en_US.srt 7.1 kB
  • 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code-en_US.srt 7.1 kB
  • 2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory-en_US.srt 7.0 kB
  • 7. Temporal Difference Learning/2. TD(0) Prediction-en_US.srt 6.8 kB
  • 10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction-en_US.srt 6.7 kB
  • 8. Approximation Methods/9. CartPole Code-en_US.srt 6.7 kB
  • 2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons-en_US.srt 6.7 kB
  • 5. Dynamic Programming/3. Designing Your RL Program-en_US.srt 6.5 kB
  • 4. Markov Decision Proccesses/7. Value Functions-en_US.srt 6.5 kB
  • 5. Dynamic Programming/14. Dynamic Programming Summary-en_US.srt 6.4 kB
  • 2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt-en_US.srt 6.3 kB
  • 7. Temporal Difference Learning/6. Q Learning-en_US.srt 6.2 kB
  • 1. Welcome/3. Where to get the Code-en_US.srt 6.2 kB
  • 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3-en_US.srt 6.0 kB
  • 7. Temporal Difference Learning/7. Q Learning in Code-en_US.srt 6.0 kB
  • 7. Temporal Difference Learning/3. TD(0) Prediction in Code-en_US.srt 5.9 kB
  • 7. Temporal Difference Learning/4. SARSA-en_US.srt 5.9 kB
  • 6. Monte Carlo/6. Monte Carlo Control without Exploring Starts-en_US.srt 5.7 kB
  • 8. Approximation Methods/1. Approximation Methods Section Introduction-en_US.srt 5.7 kB
  • 8. Approximation Methods/6. Approximation Methods for Control-en_US.srt 5.6 kB
  • 2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code-en_US.srt 5.6 kB
  • 2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program-en_US.srt 5.5 kB
  • 4. Markov Decision Proccesses/3. Choosing Rewards-en_US.srt 5.4 kB
  • 10. Stock Trading Project with Reinforcement Learning/8. Code pt 3-en_US.srt 5.3 kB
  • 8. Approximation Methods/10. Approximation Methods Exercise-en_US.srt 5.3 kB
  • 7. Temporal Difference Learning/1. Temporal Difference Introduction-en_US.srt 5.2 kB
  • 2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code-en_US.srt 5.1 kB
  • 4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2)-en_US.srt 5.0 kB
  • 2. Return of the Multi-Armed Bandit/25. Suggestion Box-en_US.srt 4.6 kB
  • 10. Stock Trading Project with Reinforcement Learning/10. Stock Trading Project Discussion-en_US.srt 4.3 kB
  • 1. Welcome/1. Introduction-en_US.srt 4.1 kB
  • 8. Approximation Methods/11. Approximation Methods Section Summary-en_US.srt 3.9 kB
  • 2. Return of the Multi-Armed Bandit/14. UCB1 Code-en_US.srt 3.7 kB
  • 14. Appendix FAQ Finale/1. What is the Appendix-en_US.srt 3.7 kB
  • 4. Markov Decision Proccesses/14. MDP Summary-en_US.srt 3.5 kB
  • 2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt-en_US.srt 3.3 kB
  • 7. Temporal Difference Learning/8. TD Learning Section Summary-en_US.srt 2.9 kB
  • 2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srt 2.9 kB
  • 2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt-en_US.srt 2.7 kB
  • 6. Monte Carlo/8. Monte Carlo Summary-en_US.srt 2.1 kB
  • 0. Websites you may like/[FCS Forum].url 133 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.ME].url 122 Bytes
  • 1. Welcome/3. External URLs.txt 75 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes

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