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[] Udemy - Advanced AI Deep Reinforcement Learning in Python

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  • 文件大小:2GB
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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. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 186MB
  4. 7. A3C/5. A3C - Code pt 4.mp4 184MB
  5. 8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 97MB
  6. 8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp4 93MB
  7. 8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp4 87MB
  8. 7. A3C/4. A3C - Code pt 3.mp4 85MB
  9. 8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp4 81MB
  10. 9. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  11. 7. A3C/1. A3C - Theory and Outline.mp4 72MB
  12. 7. A3C/3. A3C - Code pt 2.mp4 58MB
  13. 7. A3C/2. A3C - Code pt 1 (Warmup).mp4 50MB
  14. 9. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  15. 9. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  16. 9. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 38MB
  17. 9. Appendix FAQ/12. What order should I take your courses in (part 2).mp4 38MB
  18. 9. Appendix FAQ/11. What order should I take your courses in (part 1).mp4 29MB
  19. 6. Deep Q-Learning/6. Pseudocode and Replay Memory.mp4 28MB
  20. 9. Appendix FAQ/4. How to Code by Yourself (part 1).mp4 25MB
  21. 5. Policy Gradients/9. Mountain Car Continuous Theano (v2).mp4 22MB
  22. 5. Policy Gradients/7. Mountain Car Continuous Tensorflow.mp4 20MB
  23. 5. Policy Gradients/6. Mountain Car Continuous Theano.mp4 19MB
  24. 1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.mp4 19MB
  25. 5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).mp4 19MB
  26. 2. Background Review/5. Review of Temporal Difference Learning.mp4 19MB
  27. 9. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp4 18MB
  28. 5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.mp4 18MB
  29. 5. Policy Gradients/1. Policy Gradient Methods.mp4 18MB
  30. 9. Appendix FAQ/10. Is Theano Dead.mp4 18MB
  31. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.mp4 17MB
  32. 1. Introduction and Logistics/1. Introduction and Outline.mp4 16MB
  33. 6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.mp4 15MB
  34. 9. Appendix FAQ/5. How to Code by Yourself (part 2).mp4 15MB
  35. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).mp4 15MB
  36. 6. Deep Q-Learning/2. Deep Q-Learning Techniques.mp4 14MB
  37. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).mp4 14MB
  38. 6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.mp4 14MB
  39. 5. Policy Gradients/3. Policy Gradient in Theano for CartPole.mp4 13MB
  40. 2. Background Review/2. Review of Markov Decision Processes.mp4 12MB
  41. 4. TD Lambda/3. TD Lambda.mp4 12MB
  42. 2. Background Review/7. Review of Deep Learning.mp4 11MB
  43. 6. Deep Q-Learning/10. Deep Q-Learning Section Summary.mp4 10MB
  44. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.mp4 10MB
  45. 4. TD Lambda/2. N-Step in Code.mp4 9MB
  46. 7. A3C/7. Course Summary.mp4 9MB
  47. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).mp4 9MB
  48. 7. A3C/6. A3C - Section Summary.mp4 9MB
  49. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.mp4 9MB
  50. 6. Deep Q-Learning/5. Additional Implementation Details for Atari.mp4 9MB
  51. 9. Appendix FAQ/9. Python 2 vs Python 3.mp4 8MB
  52. 4. TD Lambda/4. TD Lambda in Code.mp4 8MB
  53. 6. Deep Q-Learning/9. Partially Observable MDPs.mp4 8MB
  54. 5. Policy Gradients/4. Continuous Action Spaces.mp4 7MB
  55. 2. Background Review/3. Review of Dynamic Programming.mp4 7MB
  56. 5. Policy Gradients/5. Mountain Car Continuous Specifics.mp4 7MB
  57. 2. Background Review/4. Review of Monte Carlo Methods.mp4 6MB
  58. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).mp4 6MB
  59. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.mp4 6MB
  60. 6. Deep Q-Learning/1. Deep Q-Learning Intro.mp4 6MB
  61. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.mp4 6MB
  62. 9. Appendix FAQ/1. What is the Appendix.mp4 5MB
  63. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.mp4 5MB
  64. 1. Introduction and Logistics/2. Where to get the Code.mp4 5MB
  65. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.mp4 5MB
  66. 4. TD Lambda/1. N-Step Methods.mp4 5MB
  67. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.mp4 5MB
  68. 2. Background Review/1. Review Intro.mp4 4MB
  69. 2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.mp4 4MB
  70. 4. TD Lambda/5. TD Lambda Summary.mp4 4MB
  71. 5. Policy Gradients/10. Policy Gradient Section Summary.mp4 3MB
  72. 1. Introduction and Logistics/3. How to Succeed in this Course.mp4 3MB
  73. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).mp4 3MB
  74. 9. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32KB
  75. 6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.srt 28KB
  76. 6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.srt 28KB
  77. 9. Appendix FAQ/12. What order should I take your courses in (part 2).srt 23KB
  78. 9. Appendix FAQ/4. How to Code by Yourself (part 1).srt 23KB
  79. 7. A3C/5. A3C - Code pt 4.srt 21KB
  80. 7. A3C/1. A3C - Theory and Outline.srt 20KB
  81. 9. Appendix FAQ/2. Windows-Focused Environment Setup 2018.srt 20KB
  82. 9. Appendix FAQ/11. What order should I take your courses in (part 1).srt 16KB
  83. 5. Policy Gradients/1. Policy Gradient Methods.srt 15KB
  84. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.srt 15KB
  85. 9. Appendix FAQ/6. How to Succeed in this Course (Long Version).srt 15KB
  86. 1. Introduction and Logistics/1. Introduction and Outline.srt 14KB
  87. 9. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14KB
  88. 9. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.srt 14KB
  89. 9. Appendix FAQ/5. How to Code by Yourself (part 2).srt 13KB
  90. 9. Appendix FAQ/10. Is Theano Dead.srt 13KB
  91. 6. Deep Q-Learning/2. Deep Q-Learning Techniques.srt 12KB
  92. 5. Policy Gradients/7. Mountain Car Continuous Tensorflow.srt 10KB
  93. 2. Background Review/2. Review of Markov Decision Processes.srt 10KB
  94. 5. Policy Gradients/6. Mountain Car Continuous Theano.srt 10KB
  95. 4. TD Lambda/3. TD Lambda.srt 9KB
  96. 2. Background Review/7. Review of Deep Learning.srt 9KB
  97. 7. A3C/4. A3C - Code pt 3.srt 9KB
  98. 5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.srt 9KB
  99. 5. Policy Gradients/9. Mountain Car Continuous Theano (v2).srt 8KB
  100. 7. A3C/3. A3C - Code pt 2.srt 8KB
  101. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).srt 8KB
  102. 9. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.srt 8KB
  103. 6. Deep Q-Learning/6. Pseudocode and Replay Memory.srt 8KB
  104. 7. A3C/2. A3C - Code pt 1 (Warmup).srt 8KB
  105. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.srt 8KB
  106. 8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.srt 7KB
  107. 5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).srt 7KB
  108. 6. Deep Q-Learning/5. Additional Implementation Details for Atari.srt 7KB
  109. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.srt 7KB
  110. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).srt 6KB
  111. 9. Appendix FAQ/9. Python 2 vs Python 3.srt 6KB
  112. 6. Deep Q-Learning/10. Deep Q-Learning Section Summary.srt 6KB
  113. 7. A3C/7. Course Summary.srt 6KB
  114. 8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.srt 6KB
  115. 2. Background Review/5. Review of Temporal Difference Learning.srt 6KB
  116. 6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.srt 6KB
  117. 6. Deep Q-Learning/9. Partially Observable MDPs.srt 6KB
  118. 8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.srt 6KB
  119. 6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.srt 5KB
  120. 1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.srt 5KB
  121. 2. Background Review/3. Review of Dynamic Programming.srt 5KB
  122. 5. Policy Gradients/4. Continuous Action Spaces.srt 5KB
  123. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).srt 5KB
  124. 5. Policy Gradients/5. Mountain Car Continuous Specifics.srt 5KB
  125. 2. Background Review/4. Review of Monte Carlo Methods.srt 5KB
  126. 6. Deep Q-Learning/1. Deep Q-Learning Intro.srt 5KB
  127. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.srt 5KB
  128. 1. Introduction and Logistics/2. Where to get the Code.srt 5KB
  129. 5. Policy Gradients/3. Policy Gradient in Theano for CartPole.srt 5KB
  130. 4. TD Lambda/2. N-Step in Code.srt 4KB
  131. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.srt 4KB
  132. 1. Introduction and Logistics/3. How to Succeed in this Course.srt 4KB
  133. 4. TD Lambda/1. N-Step Methods.srt 4KB
  134. 8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.srt 4KB
  135. 9. Appendix FAQ/1. What is the Appendix.srt 4KB
  136. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).srt 4KB
  137. 2. Background Review/1. Review Intro.srt 4KB
  138. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.srt 3KB
  139. 4. TD Lambda/4. TD Lambda in Code.srt 3KB
  140. 4. TD Lambda/5. TD Lambda Summary.srt 3KB
  141. 2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.srt 3KB
  142. 7. A3C/6. A3C - Section Summary.srt 3KB
  143. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.srt 2KB
  144. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).srt 2KB
  145. 3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.srt 2KB
  146. 5. Policy Gradients/10. Policy Gradient Section Summary.srt 2KB
  147. [FreeCourseWorld.Com].url 54B
  148. [DesireCourse.Net].url 51B
  149. [CourseClub.Me].url 48B