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[] Udemy - Advanced AI Deep Reinforcement Learning in Python

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文件列表

  1. 6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.mp4 235MB
  2. 6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.mp4 234MB
  3. 9. Setting Up Your Environment (FAQ by Student Request)/1. Anaconda Environment Setup.mp4 186MB
  4. 7. A3C/5. A3C - Code pt 4.mp4 184MB
  5. 2. The Basics of Reinforcement Learning/2. Elements of a Reinforcement Learning Problem.mp4 105MB
  6. 7. A3C/4. A3C - Code pt 3.mp4 85MB
  7. 8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 78MB
  8. 10. 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
  9. 8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp4 78MB
  10. 7. A3C/1. A3C - Theory and Outline.mp4 72MB
  11. 8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp4 68MB
  12. 2. The Basics of Reinforcement Learning/11. Q-Learning.mp4 67MB
  13. 8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp4 63MB
  14. 7. A3C/3. A3C - Code pt 2.mp4 58MB
  15. 2. The Basics of Reinforcement Learning/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 57MB
  16. 1. Introduction and Logistics/2. Where to get the Code.mp4 52MB
  17. 2. The Basics of Reinforcement Learning/4. Markov Decision Processes (MDPs).mp4 51MB
  18. 1. Introduction and Logistics/1. Introduction and Outline.mp4 50MB
  19. 7. A3C/2. A3C - Code pt 1 (Warmup).mp4 50MB
  20. 2. The Basics of Reinforcement Learning/6. Value Functions and the Bellman Equation.mp4 48MB
  21. 2. The Basics of Reinforcement Learning/3. States, Actions, Rewards, Policies.mp4 45MB
  22. 9. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  23. 1. Introduction and Logistics/3. How to Succeed in this Course.mp4 44MB
  24. 2. The Basics of Reinforcement Learning/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 43MB
  25. 2. The Basics of Reinforcement Learning/1. Reinforcement Learning Section Introduction.mp4 41MB
  26. 2. The Basics of Reinforcement Learning/12. How to Learn Reinforcement Learning.mp4 41MB
  27. 2. The Basics of Reinforcement Learning/10. Epsilon-Greedy.mp4 40MB
  28. 12. Appendix FAQ Finale/2. BONUS.mp4 38MB
  29. 11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 38MB
  30. 2. The Basics of Reinforcement Learning/7. What does it mean to “learn”.mp4 32MB
  31. 11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 29MB
  32. 6. Deep Q-Learning/6. Pseudocode and Replay Memory.mp4 28MB
  33. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25MB
  34. 2. The Basics of Reinforcement Learning/5. The Return.mp4 24MB
  35. 5. Policy Gradients/9. Mountain Car Continuous Theano (v2).mp4 22MB
  36. 5. Policy Gradients/7. Mountain Car Continuous Tensorflow.mp4 20MB
  37. 5. Policy Gradients/6. Mountain Car Continuous Theano.mp4 19MB
  38. 1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.mp4 19MB
  39. 5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).mp4 19MB
  40. 11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 18MB
  41. 5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.mp4 18MB
  42. 5. Policy Gradients/1. Policy Gradient Methods.mp4 18MB
  43. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.mp4 18MB
  44. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.mp4 17MB
  45. 2. The Basics of Reinforcement Learning/13. Suggestion Box.mp4 16MB
  46. 4. TD Lambda/1. N-Step Methods.mp4 16MB
  47. 6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.mp4 15MB
  48. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp4 15MB
  49. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).mp4 15MB
  50. 6. Deep Q-Learning/2. Deep Q-Learning Techniques.mp4 14MB
  51. 6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.mp4 14MB
  52. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).mp4 14MB
  53. 5. Policy Gradients/3. Policy Gradient in Theano for CartPole.mp4 13MB
  54. 4. TD Lambda/3. TD Lambda.mp4 12MB
  55. 6. Deep Q-Learning/10. Deep Q-Learning Section Summary.mp4 10MB
  56. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.mp4 10MB
  57. 4. TD Lambda/2. N-Step in Code.mp4 9MB
  58. 7. A3C/7. Course Summary.mp4 9MB
  59. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).mp4 9MB
  60. 7. A3C/6. A3C - Section Summary.mp4 9MB
  61. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.mp4 9MB
  62. 6. Deep Q-Learning/5. Additional Implementation Details for Atari.mp4 9MB
  63. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp4 8MB
  64. 4. TD Lambda/4. TD Lambda in Code.mp4 8MB
  65. 6. Deep Q-Learning/9. Partially Observable MDPs.mp4 8MB
  66. 5. Policy Gradients/4. Continuous Action Spaces.mp4 7MB
  67. 5. Policy Gradients/5. Mountain Car Continuous Specifics.mp4 7MB
  68. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).mp4 6MB
  69. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.mp4 6MB
  70. 6. Deep Q-Learning/1. Deep Q-Learning Intro.mp4 6MB
  71. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.mp4 6MB
  72. 12. Appendix FAQ Finale/1. What is the Appendix.mp4 5MB
  73. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.mp4 5MB
  74. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.mp4 5MB
  75. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.mp4 5MB
  76. 4. TD Lambda/5. TD Lambda Summary.mp4 4MB
  77. 5. Policy Gradients/10. Policy Gradient Section Summary.mp4 3MB
  78. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).mp4 3MB
  79. 6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.srt 28KB
  80. 6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.srt 28KB
  81. 2. The Basics of Reinforcement Learning/2. Elements of a Reinforcement Learning Problem.srt 27KB
  82. 11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23KB
  83. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).srt 23KB
  84. 7. A3C/5. A3C - Code pt 4.srt 21KB
  85. 7. A3C/1. A3C - Theory and Outline.srt 20KB
  86. 9. Setting Up Your Environment (FAQ by Student Request)/1. Anaconda Environment Setup.srt 20KB
  87. 2. The Basics of Reinforcement Learning/11. Q-Learning.srt 19KB
  88. 11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16KB
  89. 2. The Basics of Reinforcement Learning/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 16KB
  90. 5. Policy Gradients/1. Policy Gradient Methods.srt 15KB
  91. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.srt 15KB
  92. 11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 15KB
  93. 9. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14KB
  94. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.srt 14KB
  95. 2. The Basics of Reinforcement Learning/4. Markov Decision Processes (MDPs).srt 13KB
  96. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).srt 13KB
  97. 1. Introduction and Logistics/2. Where to get the Code.srt 13KB
  98. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.srt 13KB
  99. 2. The Basics of Reinforcement Learning/6. Value Functions and the Bellman Equation.srt 13KB
  100. 2. The Basics of Reinforcement Learning/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12KB
  101. 6. Deep Q-Learning/2. Deep Q-Learning Techniques.srt 12KB
  102. 2. The Basics of Reinforcement Learning/3. States, Actions, Rewards, Policies.srt 12KB
  103. 1. Introduction and Logistics/1. Introduction and Outline.srt 11KB
  104. 5. Policy Gradients/7. Mountain Car Continuous Tensorflow.srt 10KB
  105. 5. Policy Gradients/6. Mountain Car Continuous Theano.srt 10KB
  106. 4. TD Lambda/3. TD Lambda.srt 9KB
  107. 7. A3C/4. A3C - Code pt 3.srt 9KB
  108. 2. The Basics of Reinforcement Learning/7. What does it mean to “learn”.srt 9KB
  109. 2. The Basics of Reinforcement Learning/1. Reinforcement Learning Section Introduction.srt 9KB
  110. 5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.srt 9KB
  111. 5. Policy Gradients/9. Mountain Car Continuous Theano (v2).srt 8KB
  112. 7. A3C/3. A3C - Code pt 2.srt 8KB
  113. 1. Introduction and Logistics/3. How to Succeed in this Course.srt 8KB
  114. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).srt 8KB
  115. 12. Appendix FAQ Finale/2. BONUS.srt 8KB
  116. 2. The Basics of Reinforcement Learning/12. How to Learn Reinforcement Learning.srt 8KB
  117. 6. Deep Q-Learning/6. Pseudocode and Replay Memory.srt 8KB
  118. 7. A3C/2. A3C - Code pt 1 (Warmup).srt 8KB
  119. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.srt 8KB
  120. 2. The Basics of Reinforcement Learning/10. Epsilon-Greedy.srt 7KB
  121. 8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.srt 7KB
  122. 5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).srt 7KB
  123. 6. Deep Q-Learning/5. Additional Implementation Details for Atari.srt 7KB
  124. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.srt 7KB
  125. 2. The Basics of Reinforcement Learning/5. The Return.srt 7KB
  126. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).srt 6KB
  127. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.srt 6KB
  128. 6. Deep Q-Learning/10. Deep Q-Learning Section Summary.srt 6KB
  129. 7. A3C/7. Course Summary.srt 6KB
  130. 8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.srt 6KB
  131. 8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.srt 6KB
  132. 6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.srt 6KB
  133. 6. Deep Q-Learning/9. Partially Observable MDPs.srt 6KB
  134. 6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.srt 5KB
  135. 1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.srt 5KB
  136. 5. Policy Gradients/4. Continuous Action Spaces.srt 5KB
  137. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).srt 5KB
  138. 5. Policy Gradients/5. Mountain Car Continuous Specifics.srt 5KB
  139. 6. Deep Q-Learning/1. Deep Q-Learning Intro.srt 5KB
  140. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.srt 5KB
  141. 2. The Basics of Reinforcement Learning/13. Suggestion Box.srt 5KB
  142. 5. Policy Gradients/3. Policy Gradient in Theano for CartPole.srt 5KB
  143. 4. TD Lambda/2. N-Step in Code.srt 4KB
  144. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.srt 4KB
  145. 8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.srt 4KB
  146. 4. TD Lambda/1. N-Step Methods.srt 4KB
  147. 12. Appendix FAQ Finale/1. What is the Appendix.srt 4KB
  148. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).srt 4KB
  149. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.srt 3KB
  150. 4. TD Lambda/4. TD Lambda in Code.srt 3KB
  151. 4. TD Lambda/5. TD Lambda Summary.srt 3KB
  152. 7. A3C/6. A3C - Section Summary.srt 3KB
  153. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.srt 2KB
  154. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).srt 2KB
  155. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.srt 2KB
  156. 5. Policy Gradients/10. Policy Gradient Section Summary.srt 2KB
  157. 1. Introduction and Logistics/[Tutorialsplanet.NET].url 128B
  158. 10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/[Tutorialsplanet.NET].url 128B
  159. [Tutorialsplanet.NET].url 128B
  160. 1. Introduction and Logistics/2.1 Github Link.html 120B
  161. 11. 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 0B
  162. 11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 0B