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deeplearning

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视频 2017-10-7 20:14 2024-6-27 04:01 35 2.99 GB 155
磁力链接
magnet:?xt=urn:btih:1d1b502c756e6f850ee682d8778a2c2dddbc85bf
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjFkMWI1MDJjNzU2ZTZmODUwZWU2ODJkODc3OGEyYzJkZGRiYzg1YmZaWg==
二维码链接
deeplearning的二维码
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相关链接

deeplearning

文件列表
  1. 29 Regression Classification Intuition/160 Multiple Linear Regression Intuition.mp41.82MB
  2. 10 Building a CNN/046 Building a CNN - Step 3.mp42.23MB
  3. 26 AutoEncoders Intuition/134 A Note on Biases.mp42.37MB
  4. 09 CNN Intuition/038 Step 3 - Flattening.mp43.27MB
  5. 26 AutoEncoders Intuition/141 Deep Autoencoders.mp43.32MB
  6. 26 AutoEncoders Intuition/140 Stacked Autoencoders.mp43.99MB
  7. 23 Boltzmann Machine Intuition/105 Plan of attack.mp43.99MB
  8. 26 AutoEncoders Intuition/132 Plan of attack.mp44.01MB
  9. 19 SOMs Intuition/087 Why revisit K-Means.mp44.1MB
  10. 15 Building a RNN/072 Building a RNN - Step 7.mp44.16MB
  11. 14 RNN Intuition/058 Plan of attack.mp44.19MB
  12. 03 ANN Intuition/005 Plan of Attack.mp44.74MB
  13. 26 AutoEncoders Intuition/139 Contractive Autoencoders.mp44.98MB
  14. 21 Mega Case Study/100 Mega Case Study - Step 1.mp45.1MB
  15. 26 AutoEncoders Intuition/138 Denoising Autoencoders.mp45.3MB
  16. 19 SOMs Intuition/085 Plan of attack.mp45.35MB
  17. 29 Regression Classification Intuition/159 Simple Linear Regression Intuition - Step 2.mp45.37MB
  18. 23 Boltzmann Machine Intuition/112 Deep Boltzmann Machines.mp45.67MB
  19. 27 Building an AutoEncoder/143 Installing PyTorch.mp45.71MB
  20. 24 Building a Boltzmann Machine/114 Installing PyTorch.mp45.71MB
  21. 10 Building a CNN/045 Building a CNN - Step 2.mp45.85MB
  22. 09 CNN Intuition/033 Plan of attack.mp45.9MB
  23. 04 Building an ANN/019 Building an ANN - Step 4.mp45.95MB
  24. 31 Classification Template/171 Logistic Regression Implementation - Step 3.mp45.96MB
  25. 10 Building a CNN/042 How to get the dataset.mp46.48MB
  26. 04 Building an ANN/014 How to get the dataset.mp46.48MB
  27. 24 Building a Boltzmann Machine/113 How to get the dataset.mp46.48MB
  28. 01 Welcome to the course/003 How to get the dataset.mp46.48MB
  29. 20 Building a SOM/095 How to get the dataset.mp46.48MB
  30. 15 Building a RNN/065 How to get the dataset.mp46.48MB
  31. 27 Building an AutoEncoder/142 How to get the dataset.mp46.48MB
  32. 15 Building a RNN/075 Building a RNN - Step 10.mp46.72MB
  33. 10 Building a CNN/051 Building a CNN - Step 8.mp46.79MB
  34. 15 Building a RNN/070 Building a RNN - Step 5.mp46.86MB
  35. 04 Building an ANN/021 Building an ANN - Step 6.mp47.05MB
  36. 14 RNN Intuition/063 EXTRA LSTM Variations.mp47.33MB
  37. 26 AutoEncoders Intuition/136 Overcomplete hidden layers.mp47.66MB
  38. 15 Building a RNN/073 Building a RNN - Step 8.mp47.73MB
  39. 10 Building a CNN/043 Introduction to CNNs.mp47.84MB
  40. 09 CNN Intuition/040 Summary.mp47.91MB
  41. 15 Building a RNN/074 Building a RNN - Step 9.mp47.91MB
  42. 15 Building a RNN/067 Building a RNN - Step 2.mp47.99MB
  43. 30 Data Preprocessing Template/168 Data Preprocessing Template.mp48.13MB
  44. 31 Classification Template/170 Logistic Regression Implementation - Step 2.mp48.14MB
  45. 04 Building an ANN/018 Building an ANN - Step 3.mp48.37MB
  46. 15 Building a RNN/071 Building a RNN - Step 6.mp48.54MB
  47. 04 Building an ANN/022 Building an ANN - Step 7.mp48.99MB
  48. 15 Building a RNN/068 Building a RNN - Step 3.mp49.11MB
  49. 29 Regression Classification Intuition/158 Simple Linear Regression Intuition - Step 1.mp49.47MB
  50. 31 Classification Template/172 Logistic Regression Implementation - Step 4.mp49.68MB
  51. 10 Building a CNN/049 Building a CNN - Step 6.mp49.7MB
  52. 10 Building a CNN/048 Building a CNN - Step 5.mp49.91MB
  53. 21 Mega Case Study/101 Mega Case Study - Step 2.mp410.71MB
  54. 03 ANN Intuition/012 Backpropagation.mp410.92MB
  55. 27 Building an AutoEncoder/155 Building an AutoEncoder - Step 10.mp411.26MB
  56. 23 Boltzmann Machine Intuition/111 Deep Belief Networks.mp411.65MB
  57. 31 Classification Template/174 Classification Template.mp411.71MB
  58. 24 Building a Boltzmann Machine/121 Building a Boltzmann Machine - Step 5.mp411.8MB
  59. 27 Building an AutoEncoder/150 Building an AutoEncoder - Step 5.mp411.86MB
  60. 15 Building a RNN/069 Building a RNN - Step 4.mp412.02MB
  61. 31 Classification Template/169 Logistic Regression Implementation - Step 1.mp412.19MB
  62. 10 Building a CNN/050 Building a CNN - Step 7.mp412.57MB
  63. 26 AutoEncoders Intuition/135 Training an Auto Encoder.mp412.62MB
  64. 17 Evaluating Improving and Tuning the RNN/081 Evaluating the RNN.mp413.19MB
  65. 26 AutoEncoders Intuition/137 Sparse Autoencoders.mp413.24MB
  66. 30 Data Preprocessing Template/162 Data Preprocessing - Step 1.mp413.25MB
  67. 09 CNN Intuition/036 Step 1b - ReLU Layer.mp414.09MB
  68. 03 ANN Intuition/007 The Activation Function.mp414.75MB
  69. 16 Homework Challenge - Google Stock Price Prediction/080 Homework Solution.mp415.02MB
  70. 19 SOMs Intuition/093 EXTRA K-means Clustering part 2.mp415.36MB
  71. 24 Building a Boltzmann Machine/125 Building a Boltzmann Machine - Step 9.mp415.72MB
  72. 30 Data Preprocessing Template/163 Data Preprocessing - Step 2.mp415.85MB
  73. 04 Building an ANN/015 Business Problem Description.mp416.37MB
  74. 03 ANN Intuition/011 Stochastic Gradient Descent.mp416.82MB
  75. 04 Building an ANN/024 Building an ANN - Step 9.mp416.89MB
  76. 24 Building a Boltzmann Machine/127 Building a Boltzmann Machine - Step 11.mp417.09MB
  77. 04 Building an ANN/025 Building an ANN - Step 10.mp417.43MB
  78. 15 Building a RNN/077 Building a RNN - Step 12.mp418.18MB
  79. 04 Building an ANN/023 Building an ANN - Step 8.mp418.18MB
  80. 15 Building a RNN/064 Ethical Disclosure.mp418.27MB
  81. 03 ANN Intuition/010 Gradient Descent.mp418.53MB
  82. 19 SOMs Intuition/090 How do Self-Organizing Maps Learn Part 2.mp418.71MB
  83. 15 Building a RNN/076 Building a RNN - Step 11.mp418.97MB
  84. 19 SOMs Intuition/086 How do Self-Organizing Maps Work.mp419.08MB
  85. 10 Building a CNN/044 Building a CNN - Step 1.mp419.18MB
  86. 24 Building a Boltzmann Machine/122 Building a Boltzmann Machine - Step 6.mp419.34MB
  87. 19 SOMs Intuition/091 Live SOM example.mp419.38MB
  88. 20 Building a SOM/097 Building a SOM - Step 2.mp419.43MB
  89. 06 Evaluating Improving and Tuning the ANN/029 Improving the ANN.mp419.83MB
  90. 23 Boltzmann Machine Intuition/107 Energy-Based Models EBM.mp419.83MB
  91. 24 Building a Boltzmann Machine/119 Building a Boltzmann Machine - Step 3.mp419.97MB
  92. 27 Building an AutoEncoder/147 Building an AutoEncoder - Step 3.mp420.09MB
  93. 01 Welcome to the course/002 Installing Python.mp420.38MB
  94. 10 Building a CNN/053 Building a CNN - Step 10.mp420.54MB
  95. 30 Data Preprocessing Template/164 Data Preprocessing - Step 3.mp421.73MB
  96. 30 Data Preprocessing Template/167 Data Preprocessing - Step 6.mp422.8MB
  97. 30 Data Preprocessing Template/166 Data Preprocessing - Step 5.mp422.89MB
  98. 24 Building a Boltzmann Machine/118 Building a Boltzmann Machine - Step 2.mp423MB
  99. 19 SOMs Intuition/094 EXTRA K-means Clustering part 3.mp423.13MB
  100. 03 ANN Intuition/008 How do Neural Networks work.mp423.53MB
  101. 24 Building a Boltzmann Machine/123 Building a Boltzmann Machine - Step 7.mp423.56MB
  102. 04 Building an ANN/016 Building an ANN - Step 1.mp424.28MB
  103. 21 Mega Case Study/103 Mega Case Study - Step 4.mp424.3MB
  104. 24 Building a Boltzmann Machine/117 Building a Boltzmann Machine - Step 1.mp425.33MB
  105. 24 Building a Boltzmann Machine/126 Building a Boltzmann Machine - Step 10.mp425.99MB
  106. 03 ANN Intuition/009 How do Neural Networks learn.mp426.55MB
  107. 19 SOMs Intuition/088 K-Means Clustering Refresher.mp426.86MB
  108. 10 Building a CNN/047 Building a CNN - Step 4.mp427.19MB
  109. 26 AutoEncoders Intuition/133 Auto Encoders.mp427.22MB
  110. 27 Building an AutoEncoder/146 Building an AutoEncoder - Step 2.mp427.81MB
  111. 27 Building an AutoEncoder/156 Building an AutoEncoder - Step 11.mp428.29MB
  112. 20 Building a SOM/099 Building a SOM - Step 4.mp428.73MB
  113. 30 Data Preprocessing Template/165 Data Preprocessing - Step 4.mp428.95MB
  114. 14 RNN Intuition/060 The Vanishing Gradient Problem.mp429.01MB
  115. 23 Boltzmann Machine Intuition/106 Boltzmann Machine.mp429.14MB
  116. 29 Regression Classification Intuition/161 Logistic Regression Intuition.mp429.17MB
  117. 09 CNN Intuition/034 What are convolutional neural networks.mp429.5MB
  118. 03 ANN Intuition/006 The Neuron.mp429.57MB
  119. 04 Building an ANN/020 Building an ANN - Step 5.mp429.58MB
  120. 23 Boltzmann Machine Intuition/108 Editing Wikipedia - Our Contribution to the World.mp429.85MB
  121. 24 Building a Boltzmann Machine/124 Building a Boltzmann Machine - Step 8.mp430.01MB
  122. 19 SOMs Intuition/089 How do Self-Organizing Maps Learn Part 1.mp430.07MB
  123. 24 Building a Boltzmann Machine/115 Building a Boltzmann Machine - Introduction.mp430.3MB
  124. 15 Building a RNN/066 Building a RNN - Step 1.mp430.32MB
  125. 20 Building a SOM/096 Building a SOM - Step 1.mp430.66MB
  126. 27 Building an AutoEncoder/145 Building an AutoEncoder - Step 1.mp431MB
  127. 09 CNN Intuition/035 Step 1 - Convolution Operation.mp431.02MB
  128. 01 Welcome to the course/001 What is Deep Learning.mp431.31MB
  129. 27 Building an AutoEncoder/154 Building an AutoEncoder - Step 9.mp431.6MB
  130. 24 Building a Boltzmann Machine/128 Building a Boltzmann Machine - Step 12.mp432.1MB
  131. 23 Boltzmann Machine Intuition/110 Contrastive Divergence.mp432.38MB
  132. 09 CNN Intuition/041 Softmax Cross-Entropy.mp433.23MB
  133. 27 Building an AutoEncoder/152 Building an AutoEncoder - Step 7.mp433.7MB
  134. 27 Building an AutoEncoder/153 Building an AutoEncoder - Step 8.mp433.83MB
  135. 20 Building a SOM/098 Building a SOM - Step 3.mp436.03MB
  136. 14 RNN Intuition/059 The idea behind Recurrent Neural Networks.mp437.3MB
  137. 05 Homework Challenge - Should we say goodbye to that customer/027 Homework Solution.mp437.62MB
  138. 23 Boltzmann Machine Intuition/109 Restricted Boltzmann Machine.mp437.87MB
  139. 21 Mega Case Study/102 Mega Case Study - Step 3.mp438.16MB
  140. 27 Building an AutoEncoder/151 Building an AutoEncoder - Step 6.mp439.03MB
  141. 09 CNN Intuition/037 Step 2 - Pooling.mp440.24MB
  142. 11 Homework - Whats that pet/055 Homework Solution.mp440.96MB
  143. 24 Building a Boltzmann Machine/130 Building a Boltzmann Machine - Step 14.mp441.6MB
  144. 31 Classification Template/173 Logistic Regression Implementation - Step 5.mp442.48MB
  145. 09 CNN Intuition/039 Step 4 - Full Connection.mp442.74MB
  146. 24 Building a Boltzmann Machine/129 Building a Boltzmann Machine - Step 13.mp444.81MB
  147. 04 Building an ANN/017 Building an ANN - Step 2.mp445.86MB
  148. 14 RNN Intuition/061 LSTMs.mp445.96MB
  149. 10 Building a CNN/052 Building a CNN - Step 9.mp446.87MB
  150. 24 Building a Boltzmann Machine/120 Building a Boltzmann Machine - Step 4.mp449.35MB
  151. 27 Building an AutoEncoder/149 Building an AutoEncoder - Step 4.mp449.56MB
  152. 06 Evaluating Improving and Tuning the ANN/030 Tuning the ANN.mp450.76MB
  153. 14 RNN Intuition/062 Practical intuition.mp452.84MB
  154. 06 Evaluating Improving and Tuning the ANN/028 Evaluating the ANN.mp455.84MB
  155. 19 SOMs Intuition/092 Reading an Advanced SOM.mp456.44MB
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概述 838888磁力搜索是一个磁力链接搜索引擎，是学术研究的副产品，用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络，实时的自动采集数据，仅存储文件的标题、大小、文件列表、文件标识符（磁力链接）等基础信息 838888磁力搜索不下载任何真实资源，无法判断资源的合法性及真实性，使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源，不提供Tracker服务，不提供种子文件的下载，这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统
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