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[UdemyCourseDownloader] Zero to Deep Learning™ with Python and Keras

  • 收录时间:2019-05-31 14:54:52
  • 文件大小:2GB
  • 下载次数:50
  • 最近下载:2020-10-17 22:57:27
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文件列表

  1. 1. Welcome to the course!/5. Installation Video Guide.mp4 45MB
  2. 3. Machine Learning/22. Exercise 2 solution.mp4 43MB
  3. 3. Machine Learning/8. Linear Regression code along.mp4 40MB
  4. 3. Machine Learning/1. Section 3 Intro.mp4 39MB
  5. 3. Machine Learning/20. Exercise 1 solution.mp4 37MB
  6. 6. Convolutional Neural Networks/1. Section 6 Intro.mp4 35MB
  7. 4. Deep Learning Intro/7. Multiclass classification code along.mp4 34MB
  8. 1. Welcome to the course!/2. Introduction.mp4 34MB
  9. 5. Gradient Descent/10. Learning Rate code along.mp4 33MB
  10. 1. Welcome to the course!/8. Your first deep learning model.mp4 33MB
  11. 2. Data/5. Plotting with Matplotlib.mp4 33MB
  12. 5. Gradient Descent/17. Inner Layers Visualization code along.mp4 32MB
  13. 2. Data/3. Data exploration with Pandas code along.mp4 31MB
  14. 4. Deep Learning Intro/1. Section 4 Intro.mp4 31MB
  15. 5. Gradient Descent/1. Section 5 Intro.mp4 31MB
  16. 1. Welcome to the course!/1. Welcome to the course!.mp4 27MB
  17. 9. Improving performance/14. Movies Reviews Sentiment Analysis code along.mp4 27MB
  18. 1. Welcome to the course!/4. Download and install Anaconda.mp4 26MB
  19. 4. Deep Learning Intro/13. Exercise 2 Solution.mp4 25MB
  20. 4. Deep Learning Intro/11. Exercise 1 Solution.mp4 25MB
  21. 8. Recurrent Neural Networks/1. Section 8 Intro.mp4 24MB
  22. 9. Improving performance/19. Exercise 3 Presentation.mp4 24MB
  23. 6. Convolutional Neural Networks/22. Exercise 2 Presentation.mp4 24MB
  24. 1. Welcome to the course!/3. Real world applications of deep learning.mp4 24MB
  25. 3. Machine Learning/12. Classification code along.mp4 23MB
  26. 2. Data/1. Section 2 Intro.mp4 22MB
  27. 8. Recurrent Neural Networks/9. Rolling Windows code along.mp4 21MB
  28. 4. Deep Learning Intro/5. Neural Networks code along.mp4 21MB
  29. 4. Deep Learning Intro/15. Exercise 3 Solution.mp4 21MB
  30. 6. Convolutional Neural Networks/4. MNIST Classification code along.mp4 20MB
  31. 5. Gradient Descent/8. Numpy Arrays code along.mp4 19MB
  32. 1. Welcome to the course!/7. Course Folder Walkthrough.mp4 19MB
  33. 9. Improving performance/1. Section 9 Intro.mp4 19MB
  34. 9. Improving performance/3. Learning curves code along.mp4 19MB
  35. 3. Machine Learning/6. Cost Function code along.mp4 18MB
  36. 5. Gradient Descent/19. Exercise 1 Solution.mp4 18MB
  37. 6. Convolutional Neural Networks/20. Exercise 1 Presentation.mp4 17MB
  38. 6. Convolutional Neural Networks/7. Tensor Math code along.mp4 17MB
  39. 8. Recurrent Neural Networks/6. Time Series Forecasting code along.mp4 16MB
  40. 3. Machine Learning/18. Feature Preprocessing code along.mp4 16MB
  41. 9. Improving performance/10. Image Generator code along.mp4 15MB
  42. 9. Improving performance/5. Batch Normalization code along.mp4 15MB
  43. 6. Convolutional Neural Networks/13. Convolutional Layers code along.mp4 14MB
  44. 3. Machine Learning/11. Classification.mp4 14MB
  45. 2. Data/7. Images and Sound in Jupyter.mp4 14MB
  46. 6. Convolutional Neural Networks/17. Convolutional Neural Networks code along.mp4 14MB
  47. 4. Deep Learning Intro/17. Exercise 4 Solution.mp4 14MB
  48. 2. Data/12. Exercise 2 Solution.mp4 14MB
  49. 5. Gradient Descent/16. Initialization code along.mp4 13MB
  50. 5. Gradient Descent/12. Gradient Descent code along.mp4 13MB
  51. 6. Convolutional Neural Networks/21. Exercise 1 Solution.mp4 13MB
  52. 5. Gradient Descent/23. Exercise 3 Solution.mp4 13MB
  53. 5. Gradient Descent/6. Fully Connected Backpropagation.mp4 12MB
  54. 5. Gradient Descent/4. Chain Rule.mp4 12MB
  55. 3. Machine Learning/14. Cross Validation.mp4 12MB
  56. 6. Convolutional Neural Networks/12. Convolutional Layers.mp4 12MB
  57. 2. Data/2. Tabular data.mp4 12MB
  58. 8. Recurrent Neural Networks/5. LSTM and GRU.mp4 11MB
  59. 2. Data/6. Unstructured Data.mp4 11MB
  60. 3. Machine Learning/16. Confusion matrix.mp4 11MB
  61. 5. Gradient Descent/25. Exercise 4 Solution.mp4 11MB
  62. 5. Gradient Descent/21. Exercise 2 Solution.mp4 11MB
  63. 6. Convolutional Neural Networks/23. Exercise 2 Solution.mp4 11MB
  64. 5. Gradient Descent/15. Optimizers code along.mp4 11MB
  65. 8. Recurrent Neural Networks/10. Exercise 1 Presentation.mp4 11MB
  66. 5. Gradient Descent/2. Derivatives and Gradient.mp4 10MB
  67. 8. Recurrent Neural Networks/2. Time Series.mp4 10MB
  68. 3. Machine Learning/3. Supervised Learning.mp4 10MB
  69. 6. Convolutional Neural Networks/6. Images as Tensors.mp4 10MB
  70. 3. Machine Learning/10. Evaluating Performance code along.mp4 10MB
  71. 4. Deep Learning Intro/6. Multiple Outputs.mp4 10MB
  72. 4. Deep Learning Intro/2. Deep Learning successes.mp4 10MB
  73. 4. Deep Learning Intro/9. Feed forward.mp4 10MB
  74. 4. Deep Learning Intro/3. Neural Networks.mp4 10MB
  75. 3. Machine Learning/4. Linear Regression.mp4 10MB
  76. 3. Machine Learning/15. Cross Validation code along.mp4 10MB
  77. 8. Recurrent Neural Networks/7. Time Series Forecasting with LSTM code along.mp4 9MB
  78. 2. Data/10. Exercise 1 Solution.mp4 9MB
  79. 3. Machine Learning/13. Overfitting.mp4 9MB
  80. 3. Machine Learning/2. Machine Learning Problems.mp4 9MB
  81. 5. Gradient Descent/14. Optimizers.mp4 9MB
  82. 2. Data/4. Visual data Exploration.mp4 9MB
  83. 3. Machine Learning/9. Evaluating Performance.mp4 9MB
  84. 8. Recurrent Neural Networks/3. Sequence problems.mp4 9MB
  85. 3. Machine Learning/17. Confusion Matrix code along.mp4 9MB
  86. 5. Gradient Descent/7. Matrix Notation.mp4 9MB
  87. 4. Deep Learning Intro/8. Activation Functions.mp4 9MB
  88. 4. Deep Learning Intro/4. Deeper Networks.mp4 8MB
  89. 5. Gradient Descent/26. Tensorboard.mp4 8MB
  90. 8. Recurrent Neural Networks/12. Exercise 2 Presentation.mp4 8MB
  91. 6. Convolutional Neural Networks/2. Features from Pixels.mp4 8MB
  92. 8. Recurrent Neural Networks/11. Exercise 1 Solution.mp4 8MB
  93. 2. Data/18. Exercise 5 Solution.mp4 8MB
  94. 9. Improving performance/15. Exercise 1 Presentation.mp4 8MB
  95. 3. Machine Learning/21. Exercise 2 Presentation.mp4 7MB
  96. 5. Gradient Descent/13. EWMA.mp4 7MB
  97. 6. Convolutional Neural Networks/5. Beyond Pixels.mp4 7MB
  98. 5. Gradient Descent/3. Backpropagation intuition.mp4 7MB
  99. 5. Gradient Descent/5. Derivative Calculation.mp4 7MB
  100. 2. Data/14. Exercise 3 Solution.mp4 7MB
  101. 9. Improving performance/11. Hyperparameter search.mp4 7MB
  102. 6. Convolutional Neural Networks/10. Convolution in 2 D.mp4 7MB
  103. 6. Convolutional Neural Networks/8. Convolution in 1 D.mp4 7MB
  104. 9. Improving performance/12. Embeddings.mp4 7MB
  105. 3. Machine Learning/19. Exercise 1 Presentation.mp4 6MB
  106. 5. Gradient Descent/11. Gradient Descent.mp4 6MB
  107. 6. Convolutional Neural Networks/11. Image Filters code along.mp4 6MB
  108. 9. Improving performance/7. Dropout and Regularization code along.mp4 6MB
  109. 9. Improving performance/8. Data Augmentation.mp4 6MB
  110. 3. Machine Learning/5. Cost Function.mp4 6MB
  111. 9. Improving performance/6. Dropout.mp4 6MB
  112. 2. Data/8. Feature Engineering.mp4 6MB
  113. 9. Improving performance/13. Embeddings code along.mp4 6MB
  114. 8. Recurrent Neural Networks/4. Vanilla RNN.mp4 6MB
  115. 9. Improving performance/17. Exercise 2 Presentation.mp4 5MB
  116. 9. Improving performance/2. Learning curves.mp4 5MB
  117. 6. Convolutional Neural Networks/15. Pooling Layers code along.mp4 5MB
  118. 6. Convolutional Neural Networks/19. Beyond Images.mp4 5MB
  119. 6. Convolutional Neural Networks/18. Weights in CNNs.mp4 5MB
  120. 6. Convolutional Neural Networks/9. Convolution in 1 D code along.mp4 5MB
  121. 3. Machine Learning/7. Finding the best model.mp4 5MB
  122. 8. Recurrent Neural Networks/8. Rolling Windows.mp4 5MB
  123. 9. Improving performance/9. Continuous Learning.mp4 5MB
  124. 4. Deep Learning Intro/10. Exercise 1 Presentation.mp4 5MB
  125. 6. Convolutional Neural Networks/16. Convolutional Neural Networks.mp4 4MB
  126. 2. Data/16. Exercise 4 Solution.mp4 4MB
  127. 6. Convolutional Neural Networks/3. MNIST Classification.mp4 4MB
  128. 9. Improving performance/4. Batch Normalization.mp4 4MB
  129. 5. Gradient Descent/24. Exercise 4 Presentation.mp4 4MB
  130. 5. Gradient Descent/9. Learning Rate.mp4 4MB
  131. 5. Gradient Descent/18. Exercise 1 Presentation.mp4 4MB
  132. 2. Data/9. Exercise 1 Presentation.mp4 3MB
  133. 6. Convolutional Neural Networks/14. Pooling Layers.mp4 3MB
  134. 4. Deep Learning Intro/12. Exercise 2 Presentation.mp4 3MB
  135. 5. Gradient Descent/22. Exercise 3 Presentation.mp4 3MB
  136. 4. Deep Learning Intro/14. Exercise 3 Presentation.mp4 3MB
  137. 5. Gradient Descent/20. Exercise 2 Presentation.mp4 2MB
  138. 2. Data/17. Exercise 5 Presentation.mp4 2MB
  139. 4. Deep Learning Intro/16. Exercise 4 Presentation.mp4 2MB
  140. 2. Data/11. Exercise 2 Presentation.mp4 2MB
  141. 2. Data/13. Exercise 3 Presentation.mp4 2MB
  142. 2. Data/15. Exercise 4 Presentation.mp4 2MB
  143. 1. Welcome to the course!/5. Installation Video Guide.vtt 15KB
  144. 3. Machine Learning/20. Exercise 1 solution.vtt 11KB
  145. 3. Machine Learning/22. Exercise 2 solution.vtt 11KB
  146. 3. Machine Learning/8. Linear Regression code along.vtt 10KB
  147. 2. Data/3. Data exploration with Pandas code along.vtt 10KB
  148. 1. Welcome to the course!/3. Real world applications of deep learning.vtt 10KB
  149. 9. Improving performance/14. Movies Reviews Sentiment Analysis code along.vtt 9KB
  150. 1. Welcome to the course!/8. Your first deep learning model.vtt 9KB
  151. 4. Deep Learning Intro/7. Multiclass classification code along.vtt 8KB
  152. 5. Gradient Descent/10. Learning Rate code along.vtt 8KB
  153. 3. Machine Learning/11. Classification.vtt 8KB
  154. 3. Machine Learning/12. Classification code along.vtt 7KB
  155. 4. Deep Learning Intro/13. Exercise 2 Solution.vtt 7KB
  156. 5. Gradient Descent/17. Inner Layers Visualization code along.vtt 7KB
  157. 5. Gradient Descent/8. Numpy Arrays code along.vtt 7KB
  158. 3. Machine Learning/14. Cross Validation.vtt 7KB
  159. 4. Deep Learning Intro/11. Exercise 1 Solution.vtt 7KB
  160. 2. Data/2. Tabular data.vtt 6KB
  161. 8. Recurrent Neural Networks/5. LSTM and GRU.vtt 6KB
  162. 4. Deep Learning Intro/5. Neural Networks code along.vtt 6KB
  163. 8. Recurrent Neural Networks/6. Time Series Forecasting code along.vtt 6KB
  164. 3. Machine Learning/16. Confusion matrix.vtt 6KB
  165. 3. Machine Learning/6. Cost Function code along.vtt 6KB
  166. 6. Convolutional Neural Networks/12. Convolutional Layers.vtt 6KB
  167. 9. Improving performance/3. Learning curves code along.vtt 6KB
  168. 8. Recurrent Neural Networks/9. Rolling Windows code along.vtt 6KB
  169. 8. Recurrent Neural Networks/2. Time Series.vtt 5KB
  170. 9. Improving performance/10. Image Generator code along.vtt 5KB
  171. 6. Convolutional Neural Networks/4. MNIST Classification code along.vtt 5KB
  172. 3. Machine Learning/13. Overfitting.vtt 5KB
  173. 3. Machine Learning/9. Evaluating Performance.vtt 5KB
  174. 4. Deep Learning Intro/17. Exercise 4 Solution.vtt 5KB
  175. 6. Convolutional Neural Networks/17. Convolutional Neural Networks code along.vtt 5KB
  176. 2. Data/6. Unstructured Data.vtt 5KB
  177. 9. Improving performance/5. Batch Normalization code along.vtt 5KB
  178. 4. Deep Learning Intro/6. Multiple Outputs.vtt 5KB
  179. 6. Convolutional Neural Networks/6. Images as Tensors.vtt 5KB
  180. 6. Convolutional Neural Networks/13. Convolutional Layers code along.vtt 5KB
  181. 5. Gradient Descent/2. Derivatives and Gradient.vtt 5KB
  182. 1. Welcome to the course!/7. Course Folder Walkthrough.vtt 5KB
  183. 2. Data/4. Visual data Exploration.vtt 5KB
  184. 3. Machine Learning/18. Feature Preprocessing code along.vtt 5KB
  185. 4. Deep Learning Intro/3. Neural Networks.vtt 5KB
  186. 3. Machine Learning/3. Supervised Learning.vtt 5KB
  187. 5. Gradient Descent/19. Exercise 1 Solution.vtt 5KB
  188. 8. Recurrent Neural Networks/3. Sequence problems.vtt 5KB
  189. 4. Deep Learning Intro/9. Feed forward.vtt 5KB
  190. 5. Gradient Descent/14. Optimizers.vtt 5KB
  191. 4. Deep Learning Intro/2. Deep Learning successes.vtt 5KB
  192. 3. Machine Learning/4. Linear Regression.vtt 5KB
  193. 5. Gradient Descent/13. EWMA.vtt 4KB
  194. 4. Deep Learning Intro/8. Activation Functions.vtt 4KB
  195. 3. Machine Learning/10. Evaluating Performance code along.vtt 4KB
  196. 2. Data/7. Images and Sound in Jupyter.vtt 4KB
  197. 5. Gradient Descent/23. Exercise 3 Solution.vtt 4KB
  198. 6. Convolutional Neural Networks/21. Exercise 1 Solution.vtt 4KB
  199. 5. Gradient Descent/16. Initialization code along.vtt 4KB
  200. 5. Gradient Descent/7. Matrix Notation.vtt 4KB
  201. 9. Improving performance/11. Hyperparameter search.vtt 4KB
  202. 5. Gradient Descent/3. Backpropagation intuition.vtt 4KB
  203. 3. Machine Learning/15. Cross Validation code along.vtt 4KB
  204. 5. Gradient Descent/4. Chain Rule.vtt 4KB
  205. 5. Gradient Descent/6. Fully Connected Backpropagation.vtt 4KB
  206. 5. Gradient Descent/15. Optimizers code along.vtt 4KB
  207. 5. Gradient Descent/5. Derivative Calculation.vtt 4KB
  208. 2. Data/12. Exercise 2 Solution.vtt 4KB
  209. 4. Deep Learning Intro/4. Deeper Networks.vtt 4KB
  210. 6. Convolutional Neural Networks/5. Beyond Pixels.vtt 3KB
  211. 3. Machine Learning/2. Machine Learning Problems.vtt 3KB
  212. 5. Gradient Descent/21. Exercise 2 Solution.vtt 3KB
  213. 6. Convolutional Neural Networks/23. Exercise 2 Solution.vtt 3KB
  214. 9. Improving performance/12. Embeddings.vtt 3KB
  215. 6. Convolutional Neural Networks/2. Features from Pixels.vtt 3KB
  216. 3. Machine Learning/17. Confusion Matrix code along.vtt 3KB
  217. 5. Gradient Descent/25. Exercise 4 Solution.vtt 3KB
  218. 5. Gradient Descent/11. Gradient Descent.vtt 3KB
  219. 6. Convolutional Neural Networks/10. Convolution in 2 D.vtt 3KB
  220. 3. Machine Learning/5. Cost Function.vtt 3KB
  221. 9. Improving performance/2. Learning curves.vtt 3KB
  222. 2. Data/10. Exercise 1 Solution.vtt 3KB
  223. 5. Gradient Descent/26. Tensorboard.vtt 3KB
  224. 8. Recurrent Neural Networks/7. Time Series Forecasting with LSTM code along.vtt 3KB
  225. 4. Deep Learning Intro/15. Exercise 3 Solution.vtt 3KB
  226. 5. Gradient Descent/12. Gradient Descent code along.vtt 3KB
  227. 6. Convolutional Neural Networks/22. Exercise 2 Presentation.vtt 3KB
  228. 6. Convolutional Neural Networks/8. Convolution in 1 D.vtt 3KB
  229. 3. Machine Learning/21. Exercise 2 Presentation.vtt 3KB
  230. 8. Recurrent Neural Networks/8. Rolling Windows.vtt 3KB
  231. 9. Improving performance/8. Data Augmentation.vtt 3KB
  232. 8. Recurrent Neural Networks/4. Vanilla RNN.vtt 3KB
  233. 3. Machine Learning/19. Exercise 1 Presentation.vtt 3KB
  234. 9. Improving performance/6. Dropout.vtt 3KB
  235. 3. Machine Learning/7. Finding the best model.vtt 3KB
  236. 9. Improving performance/9. Continuous Learning.vtt 3KB
  237. 1. Welcome to the course!/4. Download and install Anaconda.vtt 3KB
  238. 2. Data/8. Feature Engineering.vtt 3KB
  239. 6. Convolutional Neural Networks/18. Weights in CNNs.vtt 3KB
  240. 6. Convolutional Neural Networks/19. Beyond Images.vtt 2KB
  241. 9. Improving performance/19. Exercise 3 Presentation.vtt 2KB
  242. 9. Improving performance/13. Embeddings code along.vtt 2KB
  243. 9. Improving performance/7. Dropout and Regularization code along.vtt 2KB
  244. 6. Convolutional Neural Networks/11. Image Filters code along.vtt 2KB
  245. 3. Machine Learning/1. Section 3 Intro.vtt 2KB
  246. 6. Convolutional Neural Networks/16. Convolutional Neural Networks.vtt 2KB
  247. 5. Gradient Descent/9. Learning Rate.vtt 2KB
  248. 7. Cloud GPUs/2. Floyd GPU notebook setup.html 2KB
  249. 1. Welcome to the course!/2. Introduction.vtt 2KB
  250. 7. Cloud GPUs/1. Google Colaboratory GPU notebook setup.html 2KB
  251. 8. Recurrent Neural Networks/11. Exercise 1 Solution.vtt 2KB
  252. 9. Improving performance/4. Batch Normalization.vtt 2KB
  253. 6. Convolutional Neural Networks/15. Pooling Layers code along.vtt 2KB
  254. 2. Data/9. Exercise 1 Presentation.vtt 2KB
  255. 6. Convolutional Neural Networks/20. Exercise 1 Presentation.vtt 2KB
  256. 5. Gradient Descent/24. Exercise 4 Presentation.vtt 2KB
  257. 2. Data/14. Exercise 3 Solution.vtt 2KB
  258. 6. Convolutional Neural Networks/1. Section 6 Intro.vtt 2KB
  259. 1. Welcome to the course!/1. Welcome to the course!.vtt 2KB
  260. 4. Deep Learning Intro/10. Exercise 1 Presentation.vtt 2KB
  261. 5. Gradient Descent/22. Exercise 3 Presentation.vtt 2KB
  262. 5. Gradient Descent/1. Section 5 Intro.vtt 2KB
  263. 4. Deep Learning Intro/1. Section 4 Intro.vtt 2KB
  264. 4. Deep Learning Intro/14. Exercise 3 Presentation.vtt 1KB
  265. 4. Deep Learning Intro/12. Exercise 2 Presentation.vtt 1KB
  266. 6. Convolutional Neural Networks/3. MNIST Classification.vtt 1KB
  267. 2. Data/18. Exercise 5 Solution.vtt 1KB
  268. 2. Data/16. Exercise 4 Solution.vtt 1KB
  269. 5. Gradient Descent/18. Exercise 1 Presentation.vtt 1KB
  270. 6. Convolutional Neural Networks/14. Pooling Layers.vtt 1KB
  271. 8. Recurrent Neural Networks/12. Exercise 2 Presentation.vtt 1KB
  272. 8. Recurrent Neural Networks/10. Exercise 1 Presentation.vtt 1KB
  273. 6. Convolutional Neural Networks/9. Convolution in 1 D code along.vtt 1KB
  274. 2. Data/17. Exercise 5 Presentation.vtt 1KB
  275. 2. Data/11. Exercise 2 Presentation.vtt 1KB
  276. 5. Gradient Descent/20. Exercise 2 Presentation.vtt 1KB
  277. 8. Recurrent Neural Networks/1. Section 8 Intro.vtt 1KB
  278. 4. Deep Learning Intro/16. Exercise 4 Presentation.vtt 1KB
  279. 1. Welcome to the course!/6. Obtain the code for the course.html 1KB
  280. 9. Improving performance/15. Exercise 1 Presentation.vtt 1KB
  281. 2. Data/1. Section 2 Intro.vtt 1KB
  282. 9. Improving performance/1. Section 9 Intro.vtt 1019B
  283. 9. Improving performance/17. Exercise 2 Presentation.vtt 984B
  284. 2. Data/13. Exercise 3 Presentation.vtt 893B
  285. 2. Data/15. Exercise 4 Presentation.vtt 784B
  286. udemycoursedownloader.com.url 132B
  287. 1. Welcome to the course!/5.2 Link to Github notebooks.html 120B
  288. 2. Data/5. Plotting with Matplotlib.vtt 111B
  289. 1. Welcome to the course!/5.1 Link to Tensorflow install docs.html 96B
  290. Udemy Course downloader.txt 94B
  291. 8. Recurrent Neural Networks/13. Exercise 2 Solution.html 26B
  292. 9. Improving performance/16. Exercise 1 Solution.html 26B
  293. 9. Improving performance/18. Exercise 2 Solution.html 26B
  294. 6. Convolutional Neural Networks/7. Tensor Math code along.vtt 8B