[Tutorialsplanet.NET] Udemy - Artificial Intelligence Reinforcement Learning in Python

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[Tutorialsplanet.NET] Udemy - Artificial Intelligence Reinforcement Learning in Python

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种子哈希：7ece9ad79a686344ca348e18aacfe308a27d4dc7
文件大小： 3.2G
已经下载：61次
下载速度：极快
收录时间：2021-04-10
最近下载：2024-05-26

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文件列表

  • 10/1. Windows-Focused Environment Setup 2018.mp4 195.4 MB
  • 4. Markov Decision Proccesses/11. Bellman Examples.mp4 91.4 MB
  • 11/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 78.1 MB
  • 5. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp4 71.8 MB
  • 9. Stock Trading Project with Reinforcement Learning/6. Code pt 2.mp4 68.5 MB
  • 1. Welcome/5. Warmup.mp4 65.7 MB
  • 4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs).mp4 64.7 MB
  • 5. Dynamic Programming/9. 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
  • 9. Stock Trading Project with Reinforcement Learning/2. 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
  • 5. Dynamic Programming/10. 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
  • 9. Stock Trading Project with Reinforcement Learning/5. Code pt 1.mp4 52.1 MB
  • 9. Stock Trading Project with Reinforcement Learning/8. Code pt 4.mp4 51.5 MB
  • 2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory.mp4 50.9 MB
  • 5. Dynamic Programming/6. Iterative Policy Evaluation for Windy Gridworld in Code.mp4 49.2 MB
  • 5. Dynamic Programming/3. Gridworld in Code.mp4 49.1 MB
  • 5. Dynamic Programming/12. Value Iteration in Code.mp4 47.9 MB
  • 9. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.mp4 47.1 MB
  • 10/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 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/5. Windy Gridworld in Code.mp4 43.5 MB
  • 2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code.mp4 43.5 MB
  • 3. High Level Overview of Reinforcement Learning/3. From Bandits to Full Reinforcement Learning.mp4 43.2 MB
  • 1. Welcome/2. Course Outline and Big Picture.mp4 41.6 MB
  • 4. Markov Decision Proccesses/6. Future Rewards.mp4 41.4 MB
  • 12/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 40.9 MB
  • 12/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
  • 13. Appendix FAQ/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
  • 13. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
  • 12/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
  • 4. Markov Decision Proccesses/1. MDP Section Introduction.mp4 39.0 MB
  • 3. High Level Overview of Reinforcement Learning/2. On Unusual or Unexpected Strategies of RL.mp4 38.9 MB
  • 2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning.mp4 36.3 MB
  • 1. Welcome/1. Introduction.mp4 35.9 MB
  • 9. Stock Trading Project with Reinforcement Learning/7. 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
  • 2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits.mp4 32.5 MB
  • 12/3. 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
  • 9. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.mp4 28.1 MB
  • 4. Markov Decision Proccesses/9. The Bellman Equation (pt 2).mp4 28.0 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
  • 11/1. 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
  • 9. Stock Trading Project with Reinforcement Learning/4. Design of the Program.mp4 24.5 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/2. Designing Your RL Program.mp4 23.4 MB
  • 4. Markov Decision Proccesses/4. The Markov Property.mp4 22.8 MB
  • 2. Return of the Multi-Armed Bandit/14. UCB1 Code.mp4 21.7 MB
  • 4. Markov Decision Proccesses/7. Value Functions.srt 19.5 MB
  • 4. Markov Decision Proccesses/7. Value Functions.mp4 19.5 MB
  • 12/1. 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
  • 2. Return of the Multi-Armed Bandit/25. Suggestion Box.mp4 16.9 MB
  • 9. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.mp4 16.6 MB
  • 4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2).mp4 16.5 MB
  • 1. Welcome/4. How to Succeed in this Course.mp4 16.5 MB
  • 11/2. How to Code by Yourself (part 2).mp4 15.5 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
  • 8. Approximation Methods/9. Course Summary and Next Steps.mp4 13.9 MB
  • 2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt.mp4 13.4 MB
  • 8. Approximation Methods/8. Semi-Gradient SARSA in Code.mp4 11.1 MB
  • 6. Monte Carlo/6. Monte Carlo Control in Code.mp4 10.7 MB
  • 6. Monte Carlo/5. Monte Carlo Control.mp4 9.7 MB
  • 7. Temporal Difference Learning/5. SARSA in Code.mp4 9.2 MB
  • 6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 9.2 MB
  • 8. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp4 8.8 MB
  • 5. Dynamic Programming/13. Dynamic Programming Summary.mp4 8.7 MB
  • 7. Temporal Difference Learning/4. SARSA.mp4 8.6 MB
  • 6. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp4 8.4 MB
  • 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 8.3 MB
  • 11/4. Python 2 vs Python 3.mp4 8.2 MB
  • 6. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp4 8.2 MB
  • 8. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp4 6.9 MB
  • 8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 6.8 MB
  • 8. Approximation Methods/1. Approximation Intro.mp4 6.8 MB
  • 8. Approximation Methods/3. Features.mp4 6.5 MB
  • 5. Dynamic Programming/11. Value Iteration.mp4 6.5 MB
  • 7. Temporal Difference Learning/2. TD(0) Prediction.mp4 6.1 MB
  • 6. Monte Carlo/9. Monte Carlo Summary.mp4 6.0 MB
  • 13. Appendix FAQ Finale/1. What is the Appendix.mp4 5.7 MB
  • 13. Appendix FAQ/1. What is the Appendix.mp4 5.7 MB
  • 7. Temporal Difference Learning/7. Q Learning in Code.mp4 5.7 MB
  • 7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 5.6 MB
  • 6. Monte Carlo/1. Monte Carlo Intro.mp4 5.2 MB
  • 5. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 5.1 MB
  • 7. Temporal Difference Learning/6. Q Learning.mp4 5.1 MB
  • 8. Approximation Methods/7. Semi-Gradient SARSA.mp4 4.9 MB
  • 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp4 4.8 MB
  • 5. Dynamic Programming/7. Policy Improvement.mp4 4.8 MB
  • 7. Temporal Difference Learning/8. TD Summary.mp4 4.1 MB
  • 5. Dynamic Programming/8. Policy Iteration.mp4 3.3 MB
  • 8. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp4 3.0 MB
  • 7. Temporal Difference Learning/1. Temporal Difference Intro.mp4 2.9 MB
  • 11/1. How to Code by Yourself (part 1).srt 30.9 kB
  • 4. Markov Decision Proccesses/11. Bellman Examples.srt 29.9 kB
  • 2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2).srt 26.3 kB
  • 12/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23.6 kB
  • 2. Return of the Multi-Armed Bandit/12. UCB1 Theory.srt 22.5 kB
  • 4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs).srt 22.4 kB
  • 10/1. Windows-Focused Environment Setup 2018.srt 20.6 kB
  • 1. Welcome/5. Warmup.srt 20.0 kB
  • 4. Markov Decision Proccesses/2. Gridworld.srt 19.6 kB
  • 11/2. How to Code by Yourself (part 2).srt 18.9 kB
  • 2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1).srt 18.8 kB
  • 10/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 18.8 kB
  • 5. Dynamic Programming/4. Iterative Policy Evaluation in Code.srt 18.5 kB
  • 5. Dynamic Programming/3. Gridworld in Code.srt 18.5 kB
  • 9. Stock Trading Project with Reinforcement Learning/2. Data and Environment.srt 17.0 kB
  • 2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory.srt 16.9 kB
  • 12/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16.4 kB
  • 8. Approximation Methods/9. Course Summary and Next Steps.srt 16.3 kB
  • 2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs.srt 15.5 kB
  • 2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma.srt 15.1 kB
  • 12/1. How to Succeed in this Course (Long Version).srt 14.9 kB
  • 4. Markov Decision Proccesses/6. Future Rewards.srt 14.5 kB
  • 11/3. Proof that using Jupyter Notebook is the same as not using it.srt 14.5 kB
  • 3. High Level Overview of Reinforcement Learning/3. From Bandits to Full Reinforcement Learning.srt 13.6 kB
  • 9. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.srt 13.3 kB
  • 9. Stock Trading Project with Reinforcement Learning/6. Code pt 2.srt 13.1 kB
  • 4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1).srt 13.1 kB
  • 5. Dynamic Programming/10. Policy Iteration in Windy Gridworld.srt 12.6 kB
  • 4. Markov Decision Proccesses/8. The Bellman Equation (pt 1).srt 12.6 kB
  • 5. Dynamic Programming/9. Policy Iteration in Code.srt 12.5 kB
  • 3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.srt 12.1 kB
  • 2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.srt 12.0 kB
  • 1. Welcome/2. Course Outline and Big Picture.srt 11.4 kB
  • 5. Dynamic Programming/5. Windy Gridworld in Code.srt 11.4 kB
  • 5. Dynamic Programming/6. Iterative Policy Evaluation for Windy Gridworld in Code.srt 11.2 kB
  • 6. Monte Carlo/2. Monte Carlo Policy Evaluation.srt 11.1 kB
  • 2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory.srt 10.7 kB
  • 9. Stock Trading Project with Reinforcement Learning/5. Code pt 1.srt 10.7 kB
  • 6. Monte Carlo/5. Monte Carlo Control.srt 10.5 kB
  • 2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits.srt 10.4 kB
  • 2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning.srt 10.3 kB
  • 5. Dynamic Programming/12. Value Iteration in Code.srt 10.1 kB
  • 7. Temporal Difference Learning/4. SARSA.srt 9.9 kB
  • 4. Markov Decision Proccesses/9. The Bellman Equation (pt 2).srt 9.7 kB
  • 5. Dynamic Programming/13. Dynamic Programming Summary.srt 9.6 kB
  • 2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code.srt 9.6 kB
  • 4. Markov Decision Proccesses/1. MDP Section Introduction.srt 9.6 kB
  • 9. Stock Trading Project with Reinforcement Learning/4. Design of the Program.srt 9.5 kB
  • 4. Markov Decision Proccesses/4. The Markov Property.srt 9.1 kB
  • 9. Stock Trading Project with Reinforcement Learning/8. Code pt 4.srt 9.0 kB
  • 4. Markov Decision Proccesses/10. The Bellman Equation (pt 3).srt 8.9 kB
  • 3. High Level Overview of Reinforcement Learning/2. On Unusual or Unexpected Strategies of RL.srt 8.8 kB
  • 2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1).srt 8.7 kB
  • 2. Return of the Multi-Armed Bandit/21. Why don't we just use a library.srt 8.6 kB
  • 13. Appendix FAQ/2. BONUS Where to get discount coupons and FREE deep learning material.srt 8.5 kB
  • 2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code.srt 8.3 kB
  • 8. Approximation Methods/1. Approximation Intro.srt 8.2 kB
  • 2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory.srt 8.1 kB
  • 13. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.srt 8.1 kB
  • 8. Approximation Methods/2. Linear Models for Reinforcement Learning.srt 7.6 kB
  • 9. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.srt 7.3 kB
  • 2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt.srt 7.3 kB
  • 6. Monte Carlo/9. Monte Carlo Summary.srt 7.3 kB
  • 5. Dynamic Programming/2. Designing Your RL Program.srt 7.2 kB
  • 2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons.srt 7.2 kB
  • 5. Dynamic Programming/11. Value Iteration.srt 7.1 kB
  • 1. Welcome/3. Where to get the Code.srt 7.1 kB
  • 8. Approximation Methods/3. Features.srt 7.1 kB
  • 7. Temporal Difference Learning/2. TD(0) Prediction.srt 6.5 kB
  • 8. Approximation Methods/6. TD(0) Semi-Gradient Prediction.srt 6.5 kB
  • 2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code.srt 6.5 kB
  • 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.srt 6.3 kB
  • 11/4. Python 2 vs Python 3.srt 6.2 kB
  • 2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program.srt 6.1 kB
  • 6. Monte Carlo/1. Monte Carlo Intro.srt 6.1 kB
  • 4. Markov Decision Proccesses/3. Choosing Rewards.srt 6.0 kB
  • 9. Stock Trading Project with Reinforcement Learning/7. Code pt 3.srt 6.0 kB
  • 6. Monte Carlo/6. Monte Carlo Control in Code.srt 6.0 kB
  • 7. Temporal Difference Learning/6. Q Learning.srt 6.0 kB
  • 2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code.srt 5.9 kB
  • 7. Temporal Difference Learning/5. SARSA in Code.srt 5.7 kB
  • 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts.srt 5.7 kB
  • 8. Approximation Methods/7. Semi-Gradient SARSA.srt 5.6 kB
  • 4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2).srt 5.6 kB
  • 8. Approximation Methods/8. Semi-Gradient SARSA in Code.srt 5.5 kB
  • 5. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.srt 5.5 kB
  • 6. Monte Carlo/4. Policy Evaluation in Windy Gridworld.srt 5.4 kB
  • 5. Dynamic Programming/7. Policy Improvement.srt 5.3 kB
  • 2. Return of the Multi-Armed Bandit/25. Suggestion Box.srt 5.2 kB
  • 7. Temporal Difference Learning/8. TD Summary.srt 4.8 kB
  • 9. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.srt 4.7 kB
  • 1. Welcome/1. Introduction.srt 4.6 kB
  • 1. Welcome/4. How to Succeed in this Course.srt 4.5 kB
  • 2. Return of the Multi-Armed Bandit/14. UCB1 Code.srt 4.4 kB
  • 8. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.srt 4.1 kB
  • 4. Markov Decision Proccesses/14. MDP Summary.srt 4.1 kB
  • 7. Temporal Difference Learning/3. TD(0) Prediction in Code.srt 4.1 kB
  • 13. Appendix FAQ/1. What is the Appendix.srt 3.9 kB
  • 2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt.srt 3.9 kB
  • 13. Appendix FAQ Finale/1. What is the Appendix.srt 3.8 kB
  • 6. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.srt 3.7 kB
  • 5. Dynamic Programming/8. Policy Iteration.srt 3.5 kB
  • 7. Temporal Difference Learning/7. Q Learning in Code.srt 3.5 kB
  • 7. Temporal Difference Learning/1. Temporal Difference Intro.srt 3.4 kB
  • 2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt.srt 3.2 kB
  • 2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt.srt 3.1 kB
  • 8. Approximation Methods/4. Monte Carlo Prediction with Approximation.srt 2.5 kB
  • 1. Welcome/[Tutorialsplanet.NET].url 128 Bytes
  • 13. Appendix FAQ Finale/[Tutorialsplanet.NET].url 128 Bytes
  • 2. Return of the Multi-Armed Bandit/[Tutorialsplanet.NET].url 128 Bytes
  • 5. Dynamic Programming/[Tutorialsplanet.NET].url 128 Bytes
  • 8. Approximation Methods/[Tutorialsplanet.NET].url 128 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes

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