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Deep Learning A-Z™ Hands-On Artificial Neural Networks

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视频 2017-8-6 11:09 2024-5-24 02:48 253 3.15 GB 155
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Deep Learning A-Z™ Hands-On Artificial Neural Networks

文件列表
  1. 1151632 - 105 - Building a Boltzmann Machine - Step 4.mp464.52MB
  2. 1151632 - 079 - Reading an Advanced SOM.mp461.87MB
  3. 1151632 - 114 - Building a Boltzmann Machine - Step 13.mp458.52MB
  4. 1151632 - 025 - Evaluating the ANN.mp455.84MB
  5. 1151632 - 115 - Building a Boltzmann Machine - Step 14.mp454.1MB
  6. 1151632 - 054 - Practical intuition.mp452.84MB
  7. 1151632 - 133 - Building an AutoEncoder - Step 6.mp452.13MB
  8. 1151632 - 027 - Tuning the ANN.mp450.76MB
  9. 1151632 - 131 - Building an AutoEncoder - Step 4.mp449.56MB
  10. 1151632 - 089 - Mega Case Study - Step 3.mp449.25MB
  11. 1151632 - 047 - Building a CNN - Step 9.mp446.87MB
  12. 1151632 - 053 - LSTMs.mp445.96MB
  13. 1151632 - 015 - Building an ANN - Step 2.mp445.86MB
  14. 1151632 - 034 - Step 4 - Full Connection.mp442.74MB
  15. 1151632 - 154 - Logistic Regression Implementation - Step 5.mp442.48MB
  16. 1151632 - 113 - Building a Boltzmann Machine - Step 12.mp441.57MB
  17. 1151632 - 049 - Homework Solution.mp440.96MB
  18. 1151632 - 032 - Step 2 - Pooling.mp440.24MB
  19. 1151632 - 109 - Building a Boltzmann Machine - Step 8.mp439.4MB
  20. 1151632 - 095 - Restricted Boltzmann Machine.mp439.25MB
  21. 1151632 - 024 - Homework Solution.mp437.62MB
  22. 1151632 - 051 - The idea behind Recurrent Neural Networks.mp437.3MB
  23. 1151632 - 128 - Building an AutoEncoder - Step 1.mp436.71MB
  24. 1151632 - 085 - Building a SOM - Step 3.mp436.03MB
  25. 1151632 - 101 - Building a Boltzmann Machine - Introduction.mp434.05MB
  26. 1151632 - 135 - Building an AutoEncoder - Step 8.mp433.83MB
  27. 1151632 - 134 - Building an AutoEncoder - Step 7.mp433.7MB
  28. 1151632 - 036 - Softmax & Cross-Entropy.mp433.23MB
  29. 1151632 - 111 - Building a Boltzmann Machine - Step 10.mp433.15MB
  30. 1151632 - 092 - Boltzmann Machine.mp431.92MB
  31. 1151632 - 136 - Building an AutoEncoder - Step 9.mp431.6MB
  32. 1151632 - 001 - What is Deep Learning .mp431.31MB
  33. 1151632 - 108 - Building a Boltzmann Machine - Step 7.mp431.19MB
  34. 1151632 - 076 - How do Self-Organizing Maps Learn (Part 1).mp431.1MB
  35. 1151632 - 030 - Step 1 - Convolution Operation.mp431.02MB
  36. 1151632 - 090 - Mega Case Study - Step 4.mp430.84MB
  37. 1151632 - 083 - Building a SOM - Step 1.mp430.66MB
  38. 1151632 - 102 - Building a Boltzmann Machine - Step 1.mp430.45MB
  39. 1151632 - 058 - Building a RNN - Step 1.mp430.32MB
  40. 1151632 - 018 - Building an ANN - Step 5.mp429.58MB
  41. 1151632 - 005 - The Neuron.mp429.57MB
  42. 1151632 - 103 - Building a Boltzmann Machine - Step 2.mp429.57MB
  43. 1151632 - 096 - Contrastive Divergence.mp429.55MB
  44. 1151632 - 029 - What are convolutional neural networks .mp429.5MB
  45. 1151632 - 142 - Logistic Regression Intuition.mp429.17MB
  46. 1151632 - 052 - The Vanishing Gradient Problem.mp429.01MB
  47. 1151632 - 146 - Data Preprocessing - Step 4.mp428.95MB
  48. 1151632 - 086 - Building a SOM - Step 4.mp428.73MB
  49. 1151632 - 138 - Building an AutoEncoder - Step 11.mp428.29MB
  50. 1151632 - 117 - Auto Encoders.mp428.19MB
  51. 1151632 - 129 - Building an AutoEncoder - Step 2.mp427.81MB
  52. 1151632 - 094 - Editing Wikipedia - Our Contribution to the World.mp427.33MB
  53. 1151632 - 042 - Building a CNN - Step 4.mp427.19MB
  54. 1151632 - 008 - How do Neural Networks learn .mp426.55MB
  55. 1151632 - 104 - Building a Boltzmann Machine - Step 3.mp425.95MB
  56. 1151632 - 107 - Building a Boltzmann Machine - Step 6.mp425.21MB
  57. 1151632 - 075 - K-Means Clustering (Refresher).mp425.01MB
  58. 1151632 - 014 - Building an ANN - Step 1.mp424.28MB
  59. 1151632 - 007 - How do Neural Networks work .mp423.53MB
  60. 1151632 - 147 - Data Preprocessing - Step 5.mp422.89MB
  61. 1151632 - 148 - Data Preprocessing - Step 6.mp422.8MB
  62. 1151632 - 112 - Building a Boltzmann Machine - Step 11.mp422.39MB
  63. 1151632 - 081 - EXTRA K-means Clustering (part 3).mp421.82MB
  64. 1151632 - 145 - Data Preprocessing - Step 3.mp421.73MB
  65. 1151632 - 048 - Building a CNN - Step 10.mp420.54MB
  66. 1151632 - 110 - Building a Boltzmann Machine - Step 9.mp420.39MB
  67. 1151632 - 002 - Installing Python.mp420.38MB
  68. 1151632 - 130 - Building an AutoEncoder - Step 3.mp420.09MB
  69. 1151632 - 073 - How do Self-Organizing Maps Work .mp420.02MB
  70. 1151632 - 026 - Improving the ANN.mp419.83MB
  71. 1151632 - 084 - Building a SOM - Step 2.mp419.43MB
  72. 1151632 - 039 - Building a CNN - Step 1.mp419.18MB
  73. 1151632 - 068 - Building a RNN - Step 11.mp418.97MB
  74. 1151632 - 077 - How do Self-Organizing Maps Learn (Part 2).mp418.65MB
  75. 1151632 - 078 - Live SOM example.mp418.54MB
  76. 1151632 - 009 - Gradient Descent.mp418.53MB
  77. 1151632 - 093 - Energy-Based Models (EBM).mp418.48MB
  78. 1151632 - 056 - Ethical Disclosure.mp418.27MB
  79. 1151632 - 021 - Building an ANN - Step 8.mp418.18MB
  80. 1151632 - 069 - Building a RNN - Step 12.mp418.18MB
  81. 1151632 - 023 - Building an ANN - Step 10.mp417.43MB
  82. 1151632 - 022 - Building an ANN - Step 9.mp416.89MB
  83. 1151632 - 010 - Stochastic Gradient Descent.mp416.82MB
  84. 1151632 - 013 - Business Problem Description.mp416.37MB
  85. 1151632 - 144 - Data Preprocessing - Step 2.mp415.85MB
  86. 1151632 - 106 - Building a Boltzmann Machine - Step 5.mp415.44MB
  87. 1151632 - 070 - Homework Solution.mp415.02MB
  88. 1151632 - 006 - The Activation Function.mp414.75MB
  89. 1151632 - 031 - Step 1(b) - ReLU Layer.mp414.09MB
  90. 1151632 - 121 - Sparse Autoencoders.mp413.99MB
  91. 1151632 - 119 - Training an Auto Encoder.mp413.55MB
  92. 1151632 - 088 - Mega Case Study - Step 2.mp413.34MB
  93. 1151632 - 143 - Data Preprocessing - Step 1.mp413.25MB
  94. 1151632 - 071 - Evaluating the RNN.mp413.19MB
  95. 1151632 - 097 - Deep Belief Networks.mp412.61MB
  96. 1151632 - 045 - Building a CNN - Step 7.mp412.57MB
  97. 1151632 - 080 - EXTRA K-means Clustering (part 2).mp412.34MB
  98. 1151632 - 150 - Logistic Regression Implementation - Step 1.mp412.19MB
  99. 1151632 - 061 - Building a RNN - Step 4.mp412.02MB
  100. 1151632 - 132 - Building an AutoEncoder - Step 5.mp411.86MB
  101. 1151632 - 155 - Classification Template.mp411.71MB
  102. 1151632 - 137 - Building an AutoEncoder - Step 10.mp411.26MB
  103. 1151632 - 063 - Building a RNN - Step 6.mp411.19MB
  104. 1151632 - 011 - Backpropagation.mp410.92MB
  105. 1151632 - 043 - Building a CNN - Step 5.mp49.91MB
  106. 1151632 - 044 - Building a CNN - Step 6.mp49.7MB
  107. 1151632 - 153 - Logistic Regression Implementation - Step 4.mp49.68MB
  108. 1151632 - 139 - Simple Linear Regression Intuition - Step 1.mp49.47MB
  109. 1151632 - 060 - Building a RNN - Step 3.mp49.11MB
  110. 1151632 - 020 - Building an ANN - Step 7.mp48.99MB
  111. 1151632 - 016 - Building an ANN - Step 3.mp48.37MB
  112. 1151632 - 151 - Logistic Regression Implementation - Step 2.mp48.14MB
  113. 1151632 - 149 - Data Preprocessing Template.mp48.13MB
  114. 1151632 - 059 - Building a RNN - Step 2.mp47.99MB
  115. 1151632 - 066 - Building a RNN - Step 9.mp47.91MB
  116. 1151632 - 035 - Summary.mp47.91MB
  117. 1151632 - 038 - Introduction to CNNs.mp47.84MB
  118. 1151632 - 065 - Building a RNN - Step 8.mp47.73MB
  119. 1151632 - 120 - Overcomplete hidden layers.mp47.64MB
  120. 1151632 - 055 - EXTRA LSTM Variations.mp47.33MB
  121. 1151632 - 019 - Building an ANN - Step 6.mp47.05MB
  122. 1151632 - 062 - Building a RNN - Step 5.mp46.86MB
  123. 1151632 - 046 - Building a CNN - Step 8.mp46.79MB
  124. 1151632 - 067 - Building a RNN - Step 10.mp46.72MB
  125. 1151632 - 126 - How to get the dataset.mp46.48MB
  126. 1151632 - 057 - How to get the dataset.mp46.48MB
  127. 1151632 - 099 - How to get the dataset.mp46.48MB
  128. 1151632 - 003 - How to get the dataset.mp46.48MB
  129. 1151632 - 082 - How to get the dataset.mp46.48MB
  130. 1151632 - 037 - How to get the dataset.mp46.48MB
  131. 1151632 - 012 - How to get the dataset.mp46.48MB
  132. 1151632 - 152 - Logistic Regression Implementation - Step 3.mp45.96MB
  133. 1151632 - 017 - Building an ANN - Step 4.mp45.95MB
  134. 1151632 - 028 - Plan of attack.mp45.93MB
  135. 1151632 - 040 - Building a CNN - Step 2.mp45.85MB
  136. 1151632 - 098 - Deep Boltzmann Machines.mp45.85MB
  137. 1151632 - 122 - Denoising Autoencoders.mp45.72MB
  138. 1151632 - 100 - Installing PyTorch.mp45.71MB
  139. 1151632 - 127 - Installing PyTorch.mp45.71MB
  140. 1151632 - 087 - Mega Case Study - Step 1.mp45.45MB
  141. 1151632 - 140 - Simple Linear Regression Intuition - Step 2.mp45.37MB
  142. 1151632 - 123 - Contractive Autoencoders.mp45.29MB
  143. 1151632 - 072 - Plan of attack.mp45.19MB
  144. 1151632 - 004 - Plan of Attack.mp44.74MB
  145. 1151632 - 124 - Stacked Autoencoders.mp44.53MB
  146. 1151632 - 050 - Plan of attack.mp44.19MB
  147. 1151632 - 064 - Building a RNN - Step 7.mp44.16MB
  148. 1151632 - 116 - Plan of attack.mp44.06MB
  149. 1151632 - 074 - Why revisit K-Means .mp44.05MB
  150. 1151632 - 091 - Plan of attack.mp43.78MB
  151. 1151632 - 125 - Deep Autoencoders.mp43.31MB
  152. 1151632 - 033 - Step 3 - Flattening.mp43.27MB
  153. 1151632 - 118 - A Note on Biases.mp42.43MB
  154. 1151632 - 041 - Building a CNN - Step 3.mp42.23MB
  155. 1151632 - 141 - Multiple Linear Regression Intuition.mp41.82MB
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