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[] Udemy - Artificial Intelligence - Reinforcement Learning in Python

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文件列表

  1. 11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 4. Markov Decision Proccesses/11. Bellman Examples.mp4 87MB
  3. 10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead.mp4 84MB
  4. 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 78MB
  5. 8. Approximation Methods/7. Approximation Methods for Control Code.mp4 78MB
  6. 2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 75MB
  7. 5. Dynamic Programming/5. Iterative Policy Evaluation in Code.mp4 68MB
  8. 10. Stock Trading Project with Reinforcement Learning/7. Code pt 2.mp4 65MB
  9. 6. Monte Carlo/5. Monte Carlo Control in Code.mp4 64MB
  10. 1. Welcome/5. Warmup.mp4 63MB
  11. 8. Approximation Methods/5. Approximation Methods for Prediction Code.mp4 62MB
  12. 4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs).mp4 62MB
  13. 5. Dynamic Programming/2. Iterative Policy Evaluation.mp4 61MB
  14. 5. Dynamic Programming/10. Policy Iteration in Code.mp4 56MB
  15. 4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1).mp4 56MB
  16. 2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 56MB
  17. 2. Return of the Multi-Armed Bandit/12. UCB1 Theory.mp4 56MB
  18. 3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp4 55MB
  19. 4. Markov Decision Proccesses/2. Gridworld.mp4 54MB
  20. 10. Stock Trading Project with Reinforcement Learning/9. Code pt 4.mp4 53MB
  21. 10. Stock Trading Project with Reinforcement Learning/3. Data and Environment.mp4 52MB
  22. 2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma.mp4 52MB
  23. 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 52MB
  24. 5. Dynamic Programming/11. Policy Iteration in Windy Gridworld.mp4 51MB
  25. 2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4 51MB
  26. 2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs.mp4 50MB
  27. 10. Stock Trading Project with Reinforcement Learning/6. Code pt 1.mp4 50MB
  28. 2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory.mp4 49MB
  29. 6. Monte Carlo/1. Monte Carlo Intro.mp4 48MB
  30. 6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 47MB
  31. 5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code.mp4 47MB
  32. 8. Approximation Methods/9. CartPole Code.mp4 47MB
  33. 5. Dynamic Programming/4. Gridworld in Code.mp4 47MB
  34. 8. Approximation Methods/3. Feature Engineering.mp4 46MB
  35. 5. Dynamic Programming/13. Value Iteration in Code.mp4 46MB
  36. 7. Temporal Difference Learning/5. SARSA in Code.mp4 45MB
  37. 10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning.mp4 45MB
  38. 5. Dynamic Programming/8. Policy Improvement.mp4 44MB
  39. 11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  40. 1. Welcome/4. How to Succeed in this Course.mp4 44MB
  41. 2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons.mp4 44MB
  42. 2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code.mp4 43MB
  43. 5. Dynamic Programming/6. Windy Gridworld in Code.mp4 41MB
  44. 2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code.mp4 41MB
  45. 3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning.mp4 41MB
  46. 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code.mp4 41MB
  47. 1. Welcome/2. Course Outline and Big Picture.mp4 40MB
  48. 4. Markov Decision Proccesses/6. Future Rewards.mp4 39MB
  49. 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 39MB
  50. 7. Temporal Difference Learning/7. Q Learning in Code.mp4 39MB
  51. 9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference.mp4 38MB
  52. 14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 38MB
  53. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 38MB
  54. 4. Markov Decision Proccesses/1. MDP Section Introduction.mp4 37MB
  55. 6. Monte Carlo/4. Monte Carlo Control.mp4 36MB
  56. 5. Dynamic Programming/12. Value Iteration.mp4 35MB
  57. 5. Dynamic Programming/1. Dynamic Programming Section Introduction.mp4 35MB
  58. 2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning.mp4 35MB
  59. 8. Approximation Methods/4. Approximation Methods for Prediction.mp4 34MB
  60. 1. Welcome/1. Introduction.mp4 34MB
  61. 5. Dynamic Programming/9. Policy Iteration.mp4 34MB
  62. 10. Stock Trading Project with Reinforcement Learning/8. Code pt 3.mp4 34MB
  63. 2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code.mp4 33MB
  64. 4. Markov Decision Proccesses/3. Choosing Rewards.mp4 32MB
  65. 7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 32MB
  66. 8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 31MB
  67. 2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits.mp4 31MB
  68. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 29MB
  69. 2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt.mp4 29MB
  70. 2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory.mp4 28MB
  71. 4. Markov Decision Proccesses/8. The Bellman Equation (pt 1).mp4 28MB
  72. 2. Return of the Multi-Armed Bandit/21. Why don't we just use a library.mp4 27MB
  73. 8. Approximation Methods/8. CartPole.mp4 27MB
  74. 10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction.mp4 27MB
  75. 4. Markov Decision Proccesses/9. The Bellman Equation (pt 2).mp4 27MB
  76. 5. Dynamic Programming/14. Dynamic Programming Summary.mp4 25MB
  77. 4. Markov Decision Proccesses/10. The Bellman Equation (pt 3).mp4 25MB
  78. 2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code.mp4 25MB
  79. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25MB
  80. 2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program.mp4 25MB
  81. 2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory.mp4 24MB
  82. 6. Monte Carlo/6. Monte Carlo Control without Exploring Starts.mp4 23MB
  83. 10. Stock Trading Project with Reinforcement Learning/5. Design of the Program.mp4 23MB
  84. 2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1).mp4 23MB
  85. 1. Welcome/3. Where to get the Code.mp4 23MB
  86. 5. Dynamic Programming/3. Designing Your RL Program.mp4 22MB
  87. 8. Approximation Methods/1. Approximation Methods Section Introduction.mp4 22MB
  88. 4. Markov Decision Proccesses/4. The Markov Property.mp4 22MB
  89. 8. Approximation Methods/11. Approximation Methods Section Summary.mp4 22MB
  90. 2. Return of the Multi-Armed Bandit/14. UCB1 Code.mp4 21MB
  91. 7. Temporal Difference Learning/6. Q Learning.mp4 20MB
  92. 4. Markov Decision Proccesses/7. Value Functions.mp4 19MB
  93. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 18MB
  94. 2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt.mp4 18MB
  95. 8. Approximation Methods/6. Approximation Methods for Control.mp4 18MB
  96. 8. Approximation Methods/10. Approximation Methods Exercise.mp4 18MB
  97. 7. Temporal Difference Learning/4. SARSA.mp4 16MB
  98. 2. Return of the Multi-Armed Bandit/25. Suggestion Box.mp4 16MB
  99. 7. Temporal Difference Learning/2. TD(0) Prediction.mp4 16MB
  100. 10. Stock Trading Project with Reinforcement Learning/10. Stock Trading Project Discussion.mp4 16MB
  101. 4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2).mp4 16MB
  102. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp4 15MB
  103. 7. Temporal Difference Learning/1. Temporal Difference Introduction.mp4 14MB
  104. 4. Markov Decision Proccesses/14. MDP Summary.mp4 14MB
  105. 2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt.mp4 14MB
  106. 2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt.mp4 13MB
  107. 6. Monte Carlo/8. Monte Carlo Summary.mp4 11MB
  108. 7. Temporal Difference Learning/8. TD Learning Section Summary.mp4 10MB
  109. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp4 8MB
  110. 14. Appendix FAQ Finale/1. What is the Appendix.mp4 5MB
  111. 4. Markov Decision Proccesses/11. Bellman Examples-en_US.srt 27KB
  112. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1)-en_US.srt 26KB
  113. 2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srt 23KB
  114. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt 22KB
  115. 5. Dynamic Programming/2. Iterative Policy Evaluation-en_US.srt 20KB
  116. 10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead-en_US.srt 20KB
  117. 11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018-en_US.srt 19KB
  118. 2. Return of the Multi-Armed Bandit/12. UCB1 Theory-en_US.srt 19KB
  119. 4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs)-en_US.srt 19KB
  120. 1. Welcome/5. Warmup-en_US.srt 18KB
  121. 4. Markov Decision Proccesses/2. Gridworld-en_US.srt 17KB
  122. 2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srt 16KB
  123. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2)-en_US.srt 16KB
  124. 5. Dynamic Programming/4. Gridworld in Code-en_US.srt 16KB
  125. 11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt 16KB
  126. 5. Dynamic Programming/5. Iterative Policy Evaluation in Code-en_US.srt 16KB
  127. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt 15KB
  128. 10. Stock Trading Project with Reinforcement Learning/3. Data and Environment-en_US.srt 15KB
  129. 2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory-en_US.srt 14KB
  130. 5. Dynamic Programming/8. Policy Improvement-en_US.srt 14KB
  131. 6. Monte Carlo/2. Monte Carlo Policy Evaluation-en_US.srt 14KB
  132. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version)-en_US.srt 14KB
  133. 2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs-en_US.srt 14KB
  134. 8. Approximation Methods/3. Feature Engineering-en_US.srt 14KB
  135. 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 14KB
  136. 2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma-en_US.srt 13KB
  137. 4. Markov Decision Proccesses/6. Future Rewards-en_US.srt 12KB
  138. 6. Monte Carlo/1. Monte Carlo Intro-en_US.srt 12KB
  139. 8. Approximation Methods/4. Approximation Methods for Prediction-en_US.srt 12KB
  140. 5. Dynamic Programming/1. Dynamic Programming Section Introduction-en_US.srt 12KB
  141. 3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning-en_US.srt 12KB
  142. 10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning-en_US.srt 12KB
  143. 10. Stock Trading Project with Reinforcement Learning/7. Code pt 2-en_US.srt 11KB
  144. 6. Monte Carlo/4. Monte Carlo Control-en_US.srt 11KB
  145. 4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srt 11KB
  146. 8. Approximation Methods/2. Linear Models for Reinforcement Learning-en_US.srt 11KB
  147. 6. Monte Carlo/5. Monte Carlo Control in Code-en_US.srt 11KB
  148. 4. Markov Decision Proccesses/8. The Bellman Equation (pt 1)-en_US.srt 11KB
  149. 5. Dynamic Programming/11. Policy Iteration in Windy Gridworld-en_US.srt 11KB
  150. 8. Approximation Methods/7. Approximation Methods for Control Code-en_US.srt 11KB
  151. 3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning-en_US.srt 11KB
  152. 2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma-en_US.srt 11KB
  153. 5. Dynamic Programming/10. Policy Iteration in Code-en_US.srt 10KB
  154. 8. Approximation Methods/5. Approximation Methods for Prediction Code-en_US.srt 10KB
  155. 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code-en_US.srt 10KB
  156. 1. Welcome/2. Course Outline and Big Picture-en_US.srt 10KB
  157. 5. Dynamic Programming/6. Windy Gridworld in Code-en_US.srt 10KB
  158. 5. Dynamic Programming/9. Policy Iteration-en_US.srt 10KB
  159. 9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference-en_US.srt 10KB
  160. 5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code-en_US.srt 9KB
  161. 5. Dynamic Programming/12. Value Iteration-en_US.srt 9KB
  162. 10. Stock Trading Project with Reinforcement Learning/6. Code pt 1-en_US.srt 9KB
  163. 2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits-en_US.srt 9KB
  164. 2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory-en_US.srt 9KB
  165. 2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning-en_US.srt 9KB
  166. 5. Dynamic Programming/13. Value Iteration in Code-en_US.srt 9KB
  167. 2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code-en_US.srt 8KB
  168. 4. Markov Decision Proccesses/9. The Bellman Equation (pt 2)-en_US.srt 8KB
  169. 10. Stock Trading Project with Reinforcement Learning/5. Design of the Program-en_US.srt 8KB
  170. 4. Markov Decision Proccesses/1. MDP Section Introduction-en_US.srt 8KB
  171. 1. Welcome/4. How to Succeed in this Course-en_US.srt 8KB
  172. 10. Stock Trading Project with Reinforcement Learning/9. Code pt 4-en_US.srt 8KB
  173. 4. Markov Decision Proccesses/4. The Markov Property-en_US.srt 8KB
  174. 14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material-en_US.srt 8KB
  175. 7. Temporal Difference Learning/5. SARSA in Code-en_US.srt 7KB
  176. 4. Markov Decision Proccesses/10. The Bellman Equation (pt 3)-en_US.srt 7KB
  177. 2. Return of the Multi-Armed Bandit/21. Why don't we just use a library-en_US.srt 7KB
  178. 2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1)-en_US.srt 7KB
  179. 8. Approximation Methods/8. CartPole-en_US.srt 7KB
  180. 2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code-en_US.srt 7KB
  181. 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code-en_US.srt 7KB
  182. 2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory-en_US.srt 7KB
  183. 7. Temporal Difference Learning/2. TD(0) Prediction-en_US.srt 7KB
  184. 10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction-en_US.srt 7KB
  185. 8. Approximation Methods/9. CartPole Code-en_US.srt 7KB
  186. 2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons-en_US.srt 7KB
  187. 5. Dynamic Programming/3. Designing Your RL Program-en_US.srt 6KB
  188. 4. Markov Decision Proccesses/7. Value Functions-en_US.srt 6KB
  189. 5. Dynamic Programming/14. Dynamic Programming Summary-en_US.srt 6KB
  190. 2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt-en_US.srt 6KB
  191. 7. Temporal Difference Learning/6. Q Learning-en_US.srt 6KB
  192. 1. Welcome/3. Where to get the Code-en_US.srt 6KB
  193. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3-en_US.srt 6KB
  194. 7. Temporal Difference Learning/7. Q Learning in Code-en_US.srt 6KB
  195. 7. Temporal Difference Learning/3. TD(0) Prediction in Code-en_US.srt 6KB
  196. 7. Temporal Difference Learning/4. SARSA-en_US.srt 6KB
  197. 6. Monte Carlo/6. Monte Carlo Control without Exploring Starts-en_US.srt 6KB
  198. 8. Approximation Methods/1. Approximation Methods Section Introduction-en_US.srt 6KB
  199. 8. Approximation Methods/6. Approximation Methods for Control-en_US.srt 5KB
  200. 2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code-en_US.srt 5KB
  201. 2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program-en_US.srt 5KB
  202. 4. Markov Decision Proccesses/3. Choosing Rewards-en_US.srt 5KB
  203. 10. Stock Trading Project with Reinforcement Learning/8. Code pt 3-en_US.srt 5KB
  204. 8. Approximation Methods/10. Approximation Methods Exercise-en_US.srt 5KB
  205. 7. Temporal Difference Learning/1. Temporal Difference Introduction-en_US.srt 5KB
  206. 2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code-en_US.srt 5KB
  207. 4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2)-en_US.srt 5KB
  208. 2. Return of the Multi-Armed Bandit/25. Suggestion Box-en_US.srt 5KB
  209. 10. Stock Trading Project with Reinforcement Learning/10. Stock Trading Project Discussion-en_US.srt 4KB
  210. 1. Welcome/1. Introduction-en_US.srt 4KB
  211. 8. Approximation Methods/11. Approximation Methods Section Summary-en_US.srt 4KB
  212. 2. Return of the Multi-Armed Bandit/14. UCB1 Code-en_US.srt 4KB
  213. 14. Appendix FAQ Finale/1. What is the Appendix-en_US.srt 4KB
  214. 4. Markov Decision Proccesses/14. MDP Summary-en_US.srt 3KB
  215. 2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt-en_US.srt 3KB
  216. 7. Temporal Difference Learning/8. TD Learning Section Summary-en_US.srt 3KB
  217. 2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srt 3KB
  218. 2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt-en_US.srt 3KB
  219. 6. Monte Carlo/8. Monte Carlo Summary-en_US.srt 2KB
  220. 0. Websites you may like/[CourseClub.ME].url 122B
  221. 10. Stock Trading Project with Reinforcement Learning/[CourseClub.Me].url 122B
  222. 3. High Level Overview of Reinforcement Learning/[CourseClub.Me].url 122B
  223. [CourseClub.Me].url 122B
  224. 1. Welcome/3. External URLs.txt 75B
  225. 0. Websites you may like/[GigaCourse.Com].url 49B
  226. 10. Stock Trading Project with Reinforcement Learning/[GigaCourse.Com].url 49B
  227. 3. High Level Overview of Reinforcement Learning/[GigaCourse.Com].url 49B
  228. [GigaCourse.Com].url 49B