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[] Udemy - Artificial Intelligence Reinforcement Learning in Python

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  • 文件大小:1GB
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文件列表

  1. 9. Appendix/2. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 3. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 104MB
  3. 4. Markov Decision Proccesses/7. Bellman Examples.mp4 87MB
  4. 9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  5. 2. Return of the Multi-Armed Bandit/7. Bayesian Thompson Sampling.mp4 52MB
  6. 9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  7. 9. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  8. 9. Appendix/11. What order should I take your courses in (part 2).mp4 38MB
  9. 9. Appendix/10. What order should I take your courses in (part 1).mp4 29MB
  10. 9. Appendix/4. How to Code by Yourself (part 1).mp4 25MB
  11. 1. Introduction and Outline/2. What is Reinforcement Learning.mp4 22MB
  12. 4. Markov Decision Proccesses/5. Value Function Introduction.mp4 20MB
  13. 9. Appendix/6. How to Succeed in this Course (Long Version).mp4 18MB
  14. 9. Appendix/5. How to Code by Yourself (part 2).mp4 15MB
  15. 8. Approximation Methods/9. Course Summary and Next Steps.mp4 13MB
  16. 3. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp4 13MB
  17. 5. Dynamic Programming/3. Iterative Policy Evaluation in Code.mp4 12MB
  18. 5. Dynamic Programming/2. Gridworld in Code.mp4 11MB
  19. 8. Approximation Methods/8. Semi-Gradient SARSA in Code.mp4 11MB
  20. 2. Return of the Multi-Armed Bandit/8. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 11MB
  21. 6. Monte Carlo/6. Monte Carlo Control in Code.mp4 10MB
  22. 1. Introduction and Outline/1. Introduction and outline.mp4 10MB
  23. 3. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp4 10MB
  24. 3. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp4 10MB
  25. 1. Introduction and Outline/4. Strategy for Passing the Course.mp4 9MB
  26. 3. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp4 9MB
  27. 6. Monte Carlo/5. Monte Carlo Control.mp4 9MB
  28. 5. Dynamic Programming/7. Policy Iteration in Windy Gridworld.mp4 9MB
  29. 3. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp4 9MB
  30. 7. Temporal Difference Learning/5. SARSA in Code.mp4 9MB
  31. 6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 9MB
  32. 8. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp4 8MB
  33. 5. Dynamic Programming/10. Dynamic Programming Summary.mp4 8MB
  34. 3. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp4 8MB
  35. 4. Markov Decision Proccesses/6. Value Functions.mp4 8MB
  36. 2. Return of the Multi-Armed Bandit/6. UCB1.mp4 8MB
  37. 7. Temporal Difference Learning/4. SARSA.mp4 8MB
  38. 6. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp4 8MB
  39. 2. Return of the Multi-Armed Bandit/4. Comparing Different Epsilons.mp4 8MB
  40. 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 8MB
  41. 9. Appendix/9. Python 2 vs Python 3.mp4 8MB
  42. 6. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp4 8MB
  43. 5. Dynamic Programming/6. Policy Iteration in Code.mp4 8MB
  44. 2. Return of the Multi-Armed Bandit/9. Nonstationary Bandits.mp4 7MB
  45. 4. Markov Decision Proccesses/2. The Markov Property.mp4 7MB
  46. 4. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp4 7MB
  47. 8. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp4 7MB
  48. 2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp4 6MB
  49. 8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 6MB
  50. 8. Approximation Methods/1. Approximation Intro.vtt 6MB
  51. 8. Approximation Methods/1. Approximation Intro.mp4 6MB
  52. 8. Approximation Methods/3. Features.mp4 6MB
  53. 5. Dynamic Programming/8. Value Iteration.mp4 6MB
  54. 3. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp4 6MB
  55. 7. Temporal Difference Learning/2. TD(0) Prediction.mp4 6MB
  56. 6. Monte Carlo/9. Monte Carlo Summary.mp4 6MB
  57. 9. Appendix/1. What is the Appendix.mp4 5MB
  58. 7. Temporal Difference Learning/7. Q Learning in Code.mp4 5MB
  59. 7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 5MB
  60. 4. Markov Decision Proccesses/4. Future Rewards.mp4 5MB
  61. 2. Return of the Multi-Armed Bandit/5. Optimistic Initial Values.mp4 5MB
  62. 3. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp4 5MB
  63. 6. Monte Carlo/1. Monte Carlo Intro.mp4 5MB
  64. 5. Dynamic Programming/9. Value Iteration in Code.mp4 5MB
  65. 7. Temporal Difference Learning/6. Q Learning.mp4 5MB
  66. 5. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 5MB
  67. 8. Approximation Methods/7. Semi-Gradient SARSA.mp4 5MB
  68. 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp4 5MB
  69. 5. Dynamic Programming/4. Policy Improvement.mp4 5MB
  70. 1. Introduction and Outline/3. Where to get the Code.mp4 4MB
  71. 3. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp4 4MB
  72. 3. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp4 4MB
  73. 9. Appendix/12. Where to get discount coupons and FREE deep learning material.mp4 4MB
  74. 7. Temporal Difference Learning/8. TD Summary.mp4 4MB
  75. 4. Markov Decision Proccesses/1. Gridworld.mp4 3MB
  76. 4. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp4 3MB
  77. 5. Dynamic Programming/5. Policy Iteration.mp4 3MB
  78. 8. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp4 3MB
  79. 2. Return of the Multi-Armed Bandit/2. Epsilon-Greedy.mp4 3MB
  80. 7. Temporal Difference Learning/1. Temporal Difference Intro.mp4 3MB
  81. 4. Markov Decision Proccesses/9. MDP Summary.mp4 2MB
  82. 2. Return of the Multi-Armed Bandit/3. Updating a Sample Mean.mp4 2MB
  83. 9. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 30KB
  84. 9. Appendix/4. How to Code by Yourself (part 1).vtt 27KB
  85. 4. Markov Decision Proccesses/7. Bellman Examples.vtt 26KB
  86. 1. Introduction and Outline/2. What is Reinforcement Learning.vtt 24KB
  87. 9. Appendix/11. What order should I take your courses in (part 2).vtt 22KB
  88. 3. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.vtt 22KB
  89. 9. Appendix/2. Windows-Focused Environment Setup 2018.vtt 19KB
  90. 9. Appendix/5. How to Code by Yourself (part 2).vtt 17KB
  91. 9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 17KB
  92. 9. Appendix/10. What order should I take your courses in (part 1).vtt 15KB
  93. 4. Markov Decision Proccesses/5. Value Function Introduction.vtt 14KB
  94. 8. Approximation Methods/9. Course Summary and Next Steps.vtt 14KB
  95. 9. Appendix/6. How to Succeed in this Course (Long Version).vtt 14KB
  96. 3. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.vtt 13KB
  97. 9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 13KB
  98. 1. Introduction and Outline/1. Introduction and outline.vtt 12KB
  99. 2. Return of the Multi-Armed Bandit/7. Bayesian Thompson Sampling.vtt 11KB
  100. 4. Markov Decision Proccesses/6. Value Functions.vtt 11KB
  101. 3. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.vtt 11KB
  102. 1. Introduction and Outline/4. Strategy for Passing the Course.vtt 11KB
  103. 3. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.vtt 10KB
  104. 5. Dynamic Programming/2. Gridworld in Code.vtt 10KB
  105. 3. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.vtt 10KB
  106. 6. Monte Carlo/2. Monte Carlo Policy Evaluation.vtt 10KB
  107. 6. Monte Carlo/5. Monte Carlo Control.vtt 9KB
  108. 3. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.vtt 9KB
  109. 5. Dynamic Programming/3. Iterative Policy Evaluation in Code.vtt 9KB
  110. 7. Temporal Difference Learning/4. SARSA.vtt 9KB
  111. 5. Dynamic Programming/10. Dynamic Programming Summary.vtt 9KB
  112. 3. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.vtt 8KB
  113. 4. Markov Decision Proccesses/2. The Markov Property.vtt 8KB
  114. 5. Dynamic Programming/7. Policy Iteration in Windy Gridworld.vtt 7KB
  115. 2. Return of the Multi-Armed Bandit/6. UCB1.vtt 7KB
  116. 4. Markov Decision Proccesses/3. Defining and Formalizing the MDP.vtt 7KB
  117. 2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.vtt 7KB
  118. 2. Return of the Multi-Armed Bandit/9. Nonstationary Bandits.vtt 7KB
  119. 8. Approximation Methods/2. Linear Models for Reinforcement Learning.vtt 7KB
  120. 3. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.vtt 7KB
  121. 6. Monte Carlo/9. Monte Carlo Summary.vtt 6KB
  122. 5. Dynamic Programming/8. Value Iteration.vtt 6KB
  123. 8. Approximation Methods/3. Features.vtt 6KB
  124. 9. Appendix/9. Python 2 vs Python 3.vtt 6KB
  125. 3. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.vtt 6KB
  126. 7. Temporal Difference Learning/2. TD(0) Prediction.vtt 6KB
  127. 8. Approximation Methods/6. TD(0) Semi-Gradient Prediction.vtt 6KB
  128. 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.vtt 6KB
  129. 5. Dynamic Programming/6. Policy Iteration in Code.vtt 6KB
  130. 2. Return of the Multi-Armed Bandit/8. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.vtt 6KB
  131. 4. Markov Decision Proccesses/4. Future Rewards.vtt 5KB
  132. 6. Monte Carlo/1. Monte Carlo Intro.vtt 5KB
  133. 7. Temporal Difference Learning/6. Q Learning.vtt 5KB
  134. 6. Monte Carlo/6. Monte Carlo Control in Code.vtt 5KB
  135. 7. Temporal Difference Learning/5. SARSA in Code.vtt 5KB
  136. 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts.vtt 5KB
  137. 8. Approximation Methods/7. Semi-Gradient SARSA.vtt 5KB
  138. 8. Approximation Methods/8. Semi-Gradient SARSA in Code.vtt 5KB
  139. 1. Introduction and Outline/3. Where to get the Code.vtt 5KB
  140. 5. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.vtt 5KB
  141. 2. Return of the Multi-Armed Bandit/4. Comparing Different Epsilons.vtt 5KB
  142. 6. Monte Carlo/4. Policy Evaluation in Windy Gridworld.vtt 5KB
  143. 5. Dynamic Programming/4. Policy Improvement.vtt 5KB
  144. 4. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.vtt 5KB
  145. 3. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.vtt 5KB
  146. 3. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.vtt 5KB
  147. 7. Temporal Difference Learning/8. TD Summary.vtt 4KB
  148. 4. Markov Decision Proccesses/1. Gridworld.vtt 4KB
  149. 8. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.vtt 4KB
  150. 7. Temporal Difference Learning/3. TD(0) Prediction in Code.vtt 4KB
  151. 9. Appendix/1. What is the Appendix.vtt 3KB
  152. 6. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.vtt 3KB
  153. 9. Appendix/12. Where to get discount coupons and FREE deep learning material.vtt 3KB
  154. 5. Dynamic Programming/5. Policy Iteration.vtt 3KB
  155. 7. Temporal Difference Learning/7. Q Learning in Code.vtt 3KB
  156. 7. Temporal Difference Learning/1. Temporal Difference Intro.vtt 3KB
  157. 5. Dynamic Programming/9. Value Iteration in Code.vtt 3KB
  158. 2. Return of the Multi-Armed Bandit/5. Optimistic Initial Values.vtt 3KB
  159. 2. Return of the Multi-Armed Bandit/2. Epsilon-Greedy.vtt 3KB
  160. 4. Markov Decision Proccesses/9. MDP Summary.vtt 2KB
  161. 8. Approximation Methods/4. Monte Carlo Prediction with Approximation.vtt 2KB
  162. 2. Return of the Multi-Armed Bandit/3. Updating a Sample Mean.vtt 2KB
  163. [FCS Forum].url 133B
  164. [FreeCourseSite.com].url 127B
  165. [CourseClub.NET].url 123B