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[] Udemy - Unsupervised Deep Learning in Python

  • 收录时间:2019-05-31 23:31:38
  • 文件大小:3GB
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文件列表

  1. 12. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 9. Applications to Recommender Systems/9. Recommender RBM Code pt 3.mp4 129MB
  3. 9. Applications to Recommender Systems/5. AutoRec in Code.mp4 102MB
  4. 10. Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 97MB
  5. 10. Basics Review/1. (Review) Theano Basics.mp4 93MB
  6. 10. Basics Review/2. (Review) Theano Neural Network in Code.mp4 87MB
  7. 9. Applications to Recommender Systems/10. Recommender RBM Code Speedup.vtt 83MB
  8. 9. Applications to Recommender Systems/10. Recommender RBM Code Speedup.mp4 83MB
  9. 10. Basics Review/3. (Review) Tensorflow Basics.mp4 81MB
  10. 12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 78MB
  11. 12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  12. 9. Applications to Recommender Systems/7. Recommender RBM Code pt 1.mp4 70MB
  13. 9. Applications to Recommender Systems/1. Recommender Systems Section Introduction.mp4 68MB
  14. 10. Basics Review/6. (Review) Keras in Code pt 1.mp4 66MB
  15. 2. Principal Components Analysis/9. PCA Application Naive Bayes.mp4 54MB
  16. 2. Principal Components Analysis/3. Why does PCA work (PCA derivation).mp4 51MB
  17. 2. Principal Components Analysis/2. How does PCA work.mp4 51MB
  18. 5. Restricted Boltzmann Machines/6. Training an RBM (part 1).mp4 49MB
  19. 9. Applications to Recommender Systems/4. AutoRec.mp4 49MB
  20. 5. Restricted Boltzmann Machines/10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.mp4 48MB
  21. 9. Applications to Recommender Systems/6. Categorical RBM for Recommender System Ratings.mp4 48MB
  22. 12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  23. 2. Principal Components Analysis/10. SVD (Singular Value Decomposition).mp4 42MB
  24. 4. Autoencoders/6. Writing the deep neural network class in code (Theano).mp4 42MB
  25. 9. Applications to Recommender Systems/8. Recommender RBM Code pt 2.mp4 40MB
  26. 5. Restricted Boltzmann Machines/2. Introduction to RBMs.mp4 39MB
  27. 12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  28. 10. Basics Review/7. (Review) Keras in Code pt 2.mp4 39MB
  29. 4. Autoencoders/4. Writing the autoencoder class in code (Theano).mp4 39MB
  30. 9. Applications to Recommender Systems/2. Why Autoencoders and RBMs work.mp4 38MB
  31. 12. Appendix/13. What order should I take your courses in (part 2).mp4 38MB
  32. 5. Restricted Boltzmann Machines/3. Motivation Behind RBMs.mp4 34MB
  33. 5. Restricted Boltzmann Machines/1. Basic Outline for RBMs.mp4 33MB
  34. 2. Principal Components Analysis/6. PCA implementation.mp4 32MB
  35. 5. Restricted Boltzmann Machines/5. Neural Network Equations.mp4 32MB
  36. 6. The Vanishing Gradient Problem/2. The Vanishing Gradient Problem Demo in Code.mp4 31MB
  37. 12. Appendix/12. What order should I take your courses in (part 1).mp4 29MB
  38. 4. Autoencoders/11. Deep Autoencoder Visualization in Code.mp4 28MB
  39. 2. Principal Components Analysis/1. What does PCA do.mp4 28MB
  40. 10. Basics Review/5. (Review) Keras Basics.mp4 28MB
  41. 5. Restricted Boltzmann Machines/8. Training an RBM (part 3) - Free Energy.mp4 28MB
  42. 5. Restricted Boltzmann Machines/7. Training an RBM (part 2).mp4 27MB
  43. 1. Introduction and Outline/4. Where to get the code and data.mp4 26MB
  44. 8. Applications to NLP (Natural Language Processing)/3. Application of t-SNE + K-Means Finding Clusters of Related Words.mp4 26MB
  45. 8. Applications to NLP (Natural Language Processing)/2. Latent Semantic Analysis in Code.mp4 26MB
  46. 4. Autoencoders/12. An Autoencoder in 1 Line of Code.mp4 25MB
  47. 12. Appendix/5. How to Code by Yourself (part 1).mp4 25MB
  48. 4. Autoencoders/7. Autoencoder in Code (Tensorflow).mp4 24MB
  49. 5. Restricted Boltzmann Machines/9. RBM Greedy Layer-Wise Pretraining.mp4 24MB
  50. 9. Applications to Recommender Systems/3. Data Preparation and Logistics.mp4 21MB
  51. 1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.mp4 19MB
  52. 4. Autoencoders/8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 19MB
  53. 12. Appendix/7. How to Succeed in this Course (Long Version).mp4 18MB
  54. 12. Appendix/11. Is Theano Dead.mp4 18MB
  55. 2. Principal Components Analysis/7. PCA for NLP.mp4 17MB
  56. 2. Principal Components Analysis/4. PCA only rotates.mp4 16MB
  57. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.mp4 15MB
  58. 12. Appendix/6. How to Code by Yourself (part 2).mp4 15MB
  59. 11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.mp4 14MB
  60. 5. Restricted Boltzmann Machines/11. RBM in Code (Tensorflow).mp4 14MB
  61. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.mp4 13MB
  62. 5. Restricted Boltzmann Machines/4. Intractability.mp4 13MB
  63. 1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.mp4 12MB
  64. 4. Autoencoders/5. Testing our Autoencoder (Theano).mp4 11MB
  65. 11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.mp4 11MB
  66. 2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.mp4 9MB
  67. 11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.mp4 9MB
  68. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.mp4 9MB
  69. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.mp4 8MB
  70. 12. Appendix/10. Python 2 vs Python 3.mp4 8MB
  71. 4. Autoencoders/9. Cross Entropy vs. KL Divergence.mp4 7MB
  72. 4. Autoencoders/3. Stacked Autoencoders.mp4 7MB
  73. 1. Introduction and Outline/3. How to Succeed in this Course.mp4 6MB
  74. 4. Autoencoders/1. Autoencoders.mp4 6MB
  75. 12. Appendix/1. What is the Appendix.mp4 5MB
  76. 6. The Vanishing Gradient Problem/1. The Vanishing Gradient Problem Description.mp4 5MB
  77. 1. Introduction and Outline/2. Where does this course fit into your deep learning studies.mp4 5MB
  78. 11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.mp4 5MB
  79. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.mp4 4MB
  80. 12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4MB
  81. 8. Applications to NLP (Natural Language Processing)/1. Application of PCA and SVD to NLP (Natural Language Processing).mp4 4MB
  82. 7. Extras + Visualizing what features a neural network has learned/1. Exercises on feature visualization and interpretation.mp4 4MB
  83. 2. Principal Components Analysis/8. PCA objective function.mp4 4MB
  84. 4. Autoencoders/2. Denoising Autoencoders.mp4 3MB
  85. 1. Introduction and Outline/1. Introduction and Outline.mp4 3MB
  86. 4. Autoencoders/10. Deep Autoencoder Visualization Description.mp4 2MB
  87. 12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28KB
  88. 12. Appendix/13. What order should I take your courses in (part 2).vtt 20KB
  89. 12. Appendix/5. How to Code by Yourself (part 1).vtt 20KB
  90. 12. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17KB
  91. 12. Appendix/12. What order should I take your courses in (part 1).vtt 14KB
  92. 12. Appendix/7. How to Succeed in this Course (Long Version).vtt 13KB
  93. 9. Applications to Recommender Systems/5. AutoRec in Code.vtt 13KB
  94. 12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12KB
  95. 2. Principal Components Analysis/2. How does PCA work.vtt 12KB
  96. 9. Applications to Recommender Systems/6. Categorical RBM for Recommender System Ratings.vtt 12KB
  97. 9. Applications to Recommender Systems/9. Recommender RBM Code pt 3.vtt 12KB
  98. 5. Restricted Boltzmann Machines/6. Training an RBM (part 1).vtt 12KB
  99. 12. Appendix/6. How to Code by Yourself (part 2).vtt 12KB
  100. 12. Appendix/11. Is Theano Dead.vtt 11KB
  101. 2. Principal Components Analysis/9. PCA Application Naive Bayes.vtt 11KB
  102. 11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.vtt 10KB
  103. 2. Principal Components Analysis/10. SVD (Singular Value Decomposition).vtt 10KB
  104. 9. Applications to Recommender Systems/7. Recommender RBM Code pt 1.vtt 9KB
  105. 4. Autoencoders/7. Autoencoder in Code (Tensorflow).vtt 8KB
  106. 10. Basics Review/5. (Review) Keras Basics.vtt 8KB
  107. 5. Restricted Boltzmann Machines/5. Neural Network Equations.vtt 7KB
  108. 5. Restricted Boltzmann Machines/8. Training an RBM (part 3) - Free Energy.vtt 7KB
  109. 5. Restricted Boltzmann Machines/10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.vtt 7KB
  110. 4. Autoencoders/11. Deep Autoencoder Visualization in Code.vtt 7KB
  111. 10. Basics Review/6. (Review) Keras in Code pt 1.vtt 6KB
  112. 5. Restricted Boltzmann Machines/7. Training an RBM (part 2).vtt 6KB
  113. 4. Autoencoders/6. Writing the deep neural network class in code (Theano).vtt 6KB
  114. 10. Basics Review/1. (Review) Theano Basics.vtt 6KB
  115. 4. Autoencoders/4. Writing the autoencoder class in code (Theano).vtt 6KB
  116. 11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.vtt 6KB
  117. 5. Restricted Boltzmann Machines/1. Basic Outline for RBMs.vtt 6KB
  118. 11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.vtt 6KB
  119. 4. Autoencoders/9. Cross Entropy vs. KL Divergence.vtt 5KB
  120. 12. Appendix/10. Python 2 vs Python 3.vtt 5KB
  121. 5. Restricted Boltzmann Machines/9. RBM Greedy Layer-Wise Pretraining.vtt 5KB
  122. 4. Autoencoders/12. An Autoencoder in 1 Line of Code.vtt 5KB
  123. 10. Basics Review/3. (Review) Tensorflow Basics.vtt 5KB
  124. 2. Principal Components Analysis/1. What does PCA do.vtt 5KB
  125. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.vtt 5KB
  126. 10. Basics Review/4. (Review) Tensorflow Neural Network in Code.vtt 5KB
  127. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.vtt 5KB
  128. 10. Basics Review/7. (Review) Keras in Code pt 2.vtt 5KB
  129. 9. Applications to Recommender Systems/8. Recommender RBM Code pt 2.vtt 5KB
  130. 4. Autoencoders/3. Stacked Autoencoders.vtt 4KB
  131. 4. Autoencoders/1. Autoencoders.vtt 4KB
  132. 2. Principal Components Analysis/7. PCA for NLP.vtt 4KB
  133. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.vtt 4KB
  134. 2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.vtt 3KB
  135. 10. Basics Review/2. (Review) Theano Neural Network in Code.vtt 3KB
  136. 12. Appendix/1. What is the Appendix.vtt 3KB
  137. 11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.vtt 3KB
  138. 12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.vtt 3KB
  139. 4. Autoencoders/5. Testing our Autoencoder (Theano).vtt 3KB
  140. 2. Principal Components Analysis/8. PCA objective function.vtt 2KB
  141. 4. Autoencoders/2. Denoising Autoencoders.vtt 2KB
  142. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.vtt 2KB
  143. 4. Autoencoders/10. Deep Autoencoder Visualization Description.vtt 2KB
  144. 4. Autoencoders/8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.vtt 2KB
  145. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.vtt 2KB
  146. 1. Introduction and Outline/1. Introduction and Outline.vtt 351B
  147. 1. Introduction and Outline/2. Where does this course fit into your deep learning studies.vtt 351B
  148. 1. Introduction and Outline/3. How to Succeed in this Course.vtt 351B
  149. 1. Introduction and Outline/4. Where to get the code and data.vtt 351B
  150. 1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.vtt 351B
  151. 1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.vtt 351B
  152. 2. Principal Components Analysis/3. Why does PCA work (PCA derivation).vtt 351B
  153. 2. Principal Components Analysis/4. PCA only rotates.vtt 351B
  154. 2. Principal Components Analysis/6. PCA implementation.vtt 351B
  155. 5. Restricted Boltzmann Machines/11. RBM in Code (Tensorflow).vtt 351B
  156. 5. Restricted Boltzmann Machines/2. Introduction to RBMs.vtt 351B
  157. 5. Restricted Boltzmann Machines/3. Motivation Behind RBMs.vtt 351B
  158. 5. Restricted Boltzmann Machines/4. Intractability.vtt 351B
  159. 6. The Vanishing Gradient Problem/1. The Vanishing Gradient Problem Description.vtt 351B
  160. 6. The Vanishing Gradient Problem/2. The Vanishing Gradient Problem Demo in Code.vtt 351B
  161. 7. Extras + Visualizing what features a neural network has learned/1. Exercises on feature visualization and interpretation.vtt 351B
  162. 8. Applications to NLP (Natural Language Processing)/1. Application of PCA and SVD to NLP (Natural Language Processing).vtt 351B
  163. 8. Applications to NLP (Natural Language Processing)/2. Latent Semantic Analysis in Code.vtt 351B
  164. 8. Applications to NLP (Natural Language Processing)/3. Application of t-SNE + K-Means Finding Clusters of Related Words.vtt 351B
  165. 9. Applications to Recommender Systems/1. Recommender Systems Section Introduction.vtt 351B
  166. 9. Applications to Recommender Systems/2. Why Autoencoders and RBMs work.vtt 351B
  167. 9. Applications to Recommender Systems/3. Data Preparation and Logistics.vtt 351B
  168. 9. Applications to Recommender Systems/4. AutoRec.vtt 351B
  169. [FCS Forum].url 133B
  170. [FreeCourseSite.com].url 127B
  171. [CourseClub.NET].url 123B