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[] Udemy - Deep Learning GANs and Variational Autoencoders

  • 收录时间:2019-06-16 11:27:16
  • 文件大小:1GB
  • 下载次数:96
  • 最近下载:2021-01-23 06:40:09
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文件列表

  1. 6. Appendix/2. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 5. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 97MB
  3. 5. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp4 93MB
  4. 5. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp4 87MB
  5. 5. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp4 81MB
  6. 6. Appendix/7. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  7. 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  8. 6. Appendix/13. What order should I take your courses in (part 2).mp4 38MB
  9. 3. Variational Autoencoders/6. Tensorflow Implementation (pt 1).mp4 34MB
  10. 4. Generative Adversarial Networks (GANs)/10. Theano Implementation.mp4 29MB
  11. 6. Appendix/12. What order should I take your courses in (part 1).mp4 29MB
  12. 4. Generative Adversarial Networks (GANs)/8. Tensorflow Implementation.mp4 28MB
  13. 4. Generative Adversarial Networks (GANs)/3. GAN Cost Function (pt 2).mp4 27MB
  14. 6. Appendix/3. How to How to install Numpy, Theano, Tensorflow, etc....mp4 27MB
  15. 3. Variational Autoencoders/2. Variational Autoencoder Architecture.vtt 27MB
  16. 3. Variational Autoencoders/7. Tensorflow Implementation (pt 2).mp4 23MB
  17. 6. Appendix/11. Is Theano Dead.mp4 18MB
  18. 2. Generative Modeling Review/5. Why do we care about generating samples.mp4 17MB
  19. 3. Variational Autoencoders/10. Theano Implementation.mp4 15MB
  20. 3. Variational Autoencoders/8. Tensorflow Implementation (pt 3).mp4 14MB
  21. 6. Appendix/5. How to Code by Yourself (part 1).mp4 14MB
  22. 6. Appendix/4. How to Succeed in this Course (Long Version).mp4 13MB
  23. 4. Generative Adversarial Networks (GANs)/7. Tensorflow Implementation Notes.mp4 12MB
  24. 3. Variational Autoencoders/3. Parameterizing a Gaussian with a Neural Network.mp4 10MB
  25. 4. Generative Adversarial Networks (GANs)/4. DCGAN.mp4 10MB
  26. 2. Generative Modeling Review/3. Gaussian Mixture Model Review.mp4 9MB
  27. 2. Generative Modeling Review/3. Gaussian Mixture Model Review.vtt 9MB
  28. 2. Generative Modeling Review/6. Neural Network and Autoencoder Review.mp4 9MB
  29. 4. Generative Adversarial Networks (GANs)/11. GAN Summary.mp4 9MB
  30. 6. Appendix/6. How to Code by Yourself (part 2).mp4 9MB
  31. 3. Variational Autoencoders/12. Bayesian Perspective.mp4 9MB
  32. 6. Appendix/10. Python 2 vs Python 3.mp4 8MB
  33. 4. Generative Adversarial Networks (GANs)/6. Fractionally-Strided Convolution.mp4 7MB
  34. 4. Generative Adversarial Networks (GANs)/5. Batch Normalization Review.mp4 7MB
  35. 4. Generative Adversarial Networks (GANs)/9. Theano Implementation Notes.mp4 7MB
  36. 3. Variational Autoencoders/5. Cost Function.mp4 7MB
  37. 2. Generative Modeling Review/8. Theano Warmup.mp4 6MB
  38. 4. Generative Adversarial Networks (GANs)/2. GAN Cost Function (pt 1).mp4 6MB
  39. 6. Appendix/1. What is the Appendix.mp4 5MB
  40. 2. Generative Modeling Review/7. Tensorflow Warmup.mp4 5MB
  41. 4. Generative Adversarial Networks (GANs)/1. GAN - Basic Principles.mp4 5MB
  42. 3. Variational Autoencoders/1. Variational Autoencoders Section Introduction.mp4 5MB
  43. 1. Introduction and Outline/4. How to succeed in this course.mp4 5MB
  44. 3. Variational Autoencoders/2. Variational Autoencoder Architecture.mp4 5MB
  45. 2. Generative Modeling Review/4. Sampling Demo Bayes Classifier with GMM.mp4 4MB
  46. 2. Generative Modeling Review/1. What does it mean to Sample.mp4 4MB
  47. 2. Generative Modeling Review/2. Sampling Demo Bayes Classifier.mp4 4MB
  48. 1. Introduction and Outline/1. Welcome.mp4 4MB
  49. 3. Variational Autoencoders/4. The Latent Space, Predictive Distributions and Samples.mp4 4MB
  50. 3. Variational Autoencoders/9. The Reparameterization Trick.mp4 4MB
  51. 1. Introduction and Outline/2. Where does this course fit into your deep learning studies.mp4 4MB
  52. 1. Introduction and Outline/3. Where to get the code and data.mp4 4MB
  53. 3. Variational Autoencoders/11. Visualizing the Latent Space.mp4 4MB
  54. 6. Appendix/9. Where to get discount coupons and FREE deep learning material.mp4 3MB
  55. 3. Variational Autoencoders/13. Variational Autoencoder Section Summary.mp4 3MB
  56. 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28KB
  57. 6. Appendix/13. What order should I take your courses in (part 2).vtt 20KB
  58. 4. Generative Adversarial Networks (GANs)/10. Theano Implementation.vtt 20KB
  59. 6. Appendix/5. How to Code by Yourself (part 1).vtt 20KB
  60. 4. Generative Adversarial Networks (GANs)/8. Tensorflow Implementation.vtt 18KB
  61. 6. Appendix/2. Windows-Focused Environment Setup 2018.vtt 17KB
  62. 4. Generative Adversarial Networks (GANs)/7. Tensorflow Implementation Notes.vtt 15KB
  63. 6. Appendix/12. What order should I take your courses in (part 1).vtt 14KB
  64. 6. Appendix/4. How to Succeed in this Course (Long Version).vtt 13KB
  65. 6. Appendix/3. How to How to install Numpy, Theano, Tensorflow, etc....vtt 12KB
  66. 6. Appendix/7. Proof that using Jupyter Notebook is the same as not using it.vtt 12KB
  67. 2. Generative Modeling Review/5. Why do we care about generating samples.vtt 12KB
  68. 6. Appendix/6. How to Code by Yourself (part 2).vtt 12KB
  69. 6. Appendix/11. Is Theano Dead.vtt 11KB
  70. 4. Generative Adversarial Networks (GANs)/11. GAN Summary.vtt 11KB
  71. 3. Variational Autoencoders/10. Theano Implementation.vtt 10KB
  72. 3. Variational Autoencoders/12. Bayesian Perspective.vtt 10KB
  73. 4. Generative Adversarial Networks (GANs)/6. Fractionally-Strided Convolution.vtt 9KB
  74. 3. Variational Autoencoders/8. Tensorflow Implementation (pt 3).vtt 9KB
  75. 4. Generative Adversarial Networks (GANs)/5. Batch Normalization Review.vtt 9KB
  76. 4. Generative Adversarial Networks (GANs)/9. Theano Implementation Notes.vtt 9KB
  77. 4. Generative Adversarial Networks (GANs)/4. DCGAN.vtt 8KB
  78. 2. Generative Modeling Review/6. Neural Network and Autoencoder Review.vtt 8KB
  79. 4. Generative Adversarial Networks (GANs)/2. GAN Cost Function (pt 1).vtt 8KB
  80. 3. Variational Autoencoders/6. Tensorflow Implementation (pt 1).vtt 8KB
  81. 3. Variational Autoencoders/5. Cost Function.vtt 8KB
  82. 3. Variational Autoencoders/3. Parameterizing a Gaussian with a Neural Network.vtt 8KB
  83. 4. Generative Adversarial Networks (GANs)/3. GAN Cost Function (pt 2).vtt 7KB
  84. 5. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.vtt 6KB
  85. 3. Variational Autoencoders/1. Variational Autoencoders Section Introduction.vtt 6KB
  86. 4. Generative Adversarial Networks (GANs)/1. GAN - Basic Principles.vtt 6KB
  87. 1. Introduction and Outline/4. How to succeed in this course.vtt 6KB
  88. 1. Introduction and Outline/2. Where does this course fit into your deep learning studies.vtt 6KB
  89. 3. Variational Autoencoders/4. The Latent Space, Predictive Distributions and Samples.vtt 6KB
  90. 6. Appendix/10. Python 2 vs Python 3.vtt 5KB
  91. 3. Variational Autoencoders/9. The Reparameterization Trick.vtt 5KB
  92. 5. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.vtt 5KB
  93. 2. Generative Modeling Review/1. What does it mean to Sample.vtt 5KB
  94. 1. Introduction and Outline/1. Welcome.vtt 5KB
  95. 5. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.vtt 5KB
  96. 2. Generative Modeling Review/8. Theano Warmup.vtt 5KB
  97. 3. Variational Autoencoders/13. Variational Autoencoder Section Summary.vtt 4KB
  98. 1. Introduction and Outline/3. Where to get the code and data.vtt 4KB
  99. 2. Generative Modeling Review/7. Tensorflow Warmup.vtt 4KB
  100. 2. Generative Modeling Review/4. Sampling Demo Bayes Classifier with GMM.vtt 4KB
  101. 5. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.vtt 3KB
  102. 2. Generative Modeling Review/2. Sampling Demo Bayes Classifier.vtt 3KB
  103. 6. Appendix/1. What is the Appendix.vtt 3KB
  104. 6. Appendix/9. Where to get discount coupons and FREE deep learning material.vtt 3KB
  105. 3. Variational Autoencoders/11. Visualizing the Latent Space.vtt 3KB
  106. 3. Variational Autoencoders/7. Tensorflow Implementation (pt 2).vtt 2KB
  107. [FCS Forum].url 133B
  108. [FreeCourseSite.com].url 127B
  109. [CourseClub.NET].url 123B