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[] Udemy - Artificial Intelligence Reinforcement Learning in Python

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文件列表

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