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[] Udemy - Advanced AI Deep Reinforcement Learning in Python

  • 收录时间:2019-03-27 11:49:14
  • 文件大小:2GB
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  • 最近下载:2020-12-17 13:30:20
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文件列表

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