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[UdemyCourseDownloader] Deep Learning with TensorFlow 2.0 [2019]

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文件列表

  1. 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4 144MB
  2. 01. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4 106MB
  3. 13. Business case/4. Preprocessing the data.mp4 92MB
  4. 13. Business case/1. Exploring the dataset and identifying predictors.mp4 78MB
  5. 13. Business case/9. Setting an early stopping mechanism.mp4 53MB
  6. 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp4 50MB
  7. 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4 49MB
  8. 12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp4 46MB
  9. 12. The MNIST example/10. Learning.mp4 44MB
  10. 03. Setting up the working environment/9. Installing TensorFlow 2.mp4 43MB
  11. 03. Setting up the working environment/2. Why Python and why Jupyter.mp4 41MB
  12. 02. Introduction to neural networks/24. N-parameter gradient descent.mp4 39MB
  13. 05. TensorFlow - An introduction/1. TensorFlow outline.mp4 38MB
  14. 02. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 38MB
  15. 05. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 38MB
  16. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4 38MB
  17. 13. Business case/3. Balancing the dataset.mp4 35MB
  18. 03. Setting up the working environment/4. Installing Anaconda.mp4 35MB
  19. 13. Business case/8. Learning and interpreting the result.mp4 35MB
  20. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4 34MB
  21. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 34MB
  22. 05. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp4 33MB
  23. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 33MB
  24. 12. The MNIST example/13. Testing the model.mp4 32MB
  25. 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp4 32MB
  26. 12. The MNIST example/8. Outline the model.mp4 31MB
  27. 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4 27MB
  28. 05. TensorFlow - An introduction/2. TensorFlow 2 intro.mp4 25MB
  29. 05. TensorFlow - An introduction/7. Cutomizing your model.mp4 25MB
  30. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 24MB
  31. 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 23MB
  32. 03. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 21MB
  33. 04. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 21MB
  34. 12. The MNIST example/2. How to tackle the MNIST.mp4 20MB
  35. 13. Business case/6. Load the preprocessed data.mp4 19MB
  36. 05. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp4 19MB
  37. 02. Introduction to neural networks/22. One parameter gradient descent.mp4 18MB
  38. 12. The MNIST example/3. Importing the relevant packages and load the data.mp4 18MB
  39. 01. Welcome! Course introduction/2. What does the course cover.mp4 16MB
  40. 12. The MNIST example/1. The dataset.mp4 16MB
  41. 12. The MNIST example/9. Select the loss and the optimizer.mp4 15MB
  42. 15. Conclusion/1. See how much you have learned.mp4 14MB
  43. 02. Introduction to neural networks/1. Introduction to neural networks.mp4 14MB
  44. 06. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 13MB
  45. 02. Introduction to neural networks/5. Types of machine learning.mp4 12MB
  46. 13. Business case/11. Testing the model.mp4 12MB
  47. 02. Introduction to neural networks/20. Cross-entropy loss.mp4 11MB
  48. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4 11MB
  49. 06. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11MB
  50. 08. Overfitting/1. Underfitting and overfitting.mp4 11MB
  51. 15. Conclusion/3. An overview of CNNs.mp4 11MB
  52. 04. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp4 11MB
  53. 10. Gradient descent and learning rates/4. Learning rate schedules.mp4 10MB
  54. 04. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4 10MB
  55. 03. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 10MB
  56. 08. Overfitting/6. Early stopping.mp4 9MB
  57. 10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4 9MB
  58. 08. Overfitting/3. Training and validation.mp4 9MB
  59. 02. Introduction to neural networks/7. The linear model.mp4 9MB
  60. 06. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4 9MB
  61. 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp4 9MB
  62. 02. Introduction to neural networks/3. Training the model.mp4 9MB
  63. 06. Going deeper Introduction to deep neural networks/5. Activation functions.mp4 9MB
  64. 11. Preprocessing/1. Preprocessing introduction.mp4 8MB
  65. 11. Preprocessing/3. Standardization.mp4 8MB
  66. 09. Initialization/1. Initialization - Introduction.mp4 8MB
  67. 13. Business case/2. Outlining the business case solution.mp4 8MB
  68. 15. Conclusion/6. An overview of non-NN approaches.mp4 8MB
  69. 10. Gradient descent and learning rates/7. Adaptive moment estimation.mp4 8MB
  70. 02. Introduction to neural networks/10. The linear model. Multiple inputs.mp4 8MB
  71. 08. Overfitting/4. Training, validation, and test.mp4 7MB
  72. 06. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7MB
  73. 02. Introduction to neural networks/18. L2-norm loss.mp4 7MB
  74. 03. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 7MB
  75. 05. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp4 7MB
  76. 08. Overfitting/5. N-fold cross validation.mp4 7MB
  77. 06. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4 7MB
  78. 08. Overfitting/2. Underfitting and overfitting - classification.mp4 7MB
  79. 06. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 7MB
  80. 04. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 7MB
  81. 02. Introduction to neural networks/14. Graphical representation.mp4 6MB
  82. 15. Conclusion/2. What’s further out there in the machine and deep learning world.mp4 6MB
  83. 11. Preprocessing/5. One-hot and binary encoding.mp4 6MB
  84. 10. Gradient descent and learning rates/3. Momentum.mp4 6MB
  85. 11. Preprocessing/4. Dealing with categorical data.mp4 6MB
  86. 09. Initialization/3. Xavier initialization.mp4 6MB
  87. 02. Introduction to neural networks/16. The objective function.mp4 6MB
  88. 09. Initialization/2. Types of simple initializations.mp4 6MB
  89. 15. Conclusion/5. An overview of RNNs.mp4 5MB
  90. 06. Going deeper Introduction to deep neural networks/1. Layers.mp4 5MB
  91. 10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp4 4MB
  92. 11. Preprocessing/2. Basic preprocessing.mp4 4MB
  93. 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp4 3MB
  94. 06. Going deeper Introduction to deep neural networks/1.1 Course Notes - Section 6.pdf.pdf 936KB
  95. 06. Going deeper Introduction to deep neural networks/2.1 Course Notes - Section 6.pdf.pdf 936KB
  96. 02. Introduction to neural networks/1.1 Course Notes - Section 2.pdf.pdf 928KB
  97. 02. Introduction to neural networks/3.1 Course Notes - Section 2.pdf.pdf 928KB
  98. 02. Introduction to neural networks/5.1 Course Notes - Section 2.pdf.pdf 928KB
  99. 02. Introduction to neural networks/7.1 Course Notes - Section 2.pdf.pdf 928KB
  100. 02. Introduction to neural networks/10.1 Course Notes - Section 2.pdf.pdf 928KB
  101. 02. Introduction to neural networks/12.1 Course Notes - Section 2.pdf.pdf 928KB
  102. 02. Introduction to neural networks/14.1 Course Notes - Section 2.pdf.pdf 928KB
  103. 02. Introduction to neural networks/16.1 Course Notes - Section 2.pdf.pdf 928KB
  104. 02. Introduction to neural networks/18.1 Course Notes - Section 2.pdf.pdf 928KB
  105. 02. Introduction to neural networks/20.1 Course Notes - Section 2.pdf.pdf 928KB
  106. 02. Introduction to neural networks/22.1 Course Notes - Section 2.pdf.pdf 928KB
  107. 02. Introduction to neural networks/24.1 Course Notes - Section 2.pdf.pdf 928KB
  108. 13. Business case/1.1 Audiobooks_data.csv.csv 625KB
  109. 13. Business case/4.3 Audiobooks_data.csv.csv 625KB
  110. 13. Business case/5.2 Audiobooks_data.csv.csv 625KB
  111. 03. Setting up the working environment/7.1 Shortcuts for Jupyter.pdf.pdf 619KB
  112. 07. Backpropagation. A peek into the Mathematics of Optimization/1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf 182KB
  113. 02. Introduction to neural networks/22.2 GD-function-example.xlsx.xlsx 42KB
  114. 13. Business case/4. Preprocessing the data.vtt 11KB
  115. 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.vtt 10KB
  116. 04. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.vtt 9KB
  117. 13. Business case/1. Exploring the dataset and identifying predictors.vtt 9KB
  118. 01. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.vtt 9KB
  119. 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.vtt 8KB
  120. 12. The MNIST example/6. Preprocess the data - shuffle and batch the data.vtt 8KB
  121. 02. Introduction to neural networks/22. One parameter gradient descent.vtt 7KB
  122. 12. The MNIST example/10. Learning.vtt 7KB
  123. 13. Business case/9. Setting an early stopping mechanism.vtt 7KB
  124. 05. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.vtt 7KB
  125. 02. Introduction to neural networks/24. N-parameter gradient descent.vtt 7KB
  126. 12. The MNIST example/8. Outline the model.vtt 6KB
  127. 03. Setting up the working environment/9. Installing TensorFlow 2.vtt 6KB
  128. 08. Overfitting/6. Early stopping.vtt 6KB
  129. 03. Setting up the working environment/6. The Jupyter dashboard - part 2.vtt 6KB
  130. 04. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.vtt 6KB
  131. 06. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.vtt 6KB
  132. 15. Conclusion/3. An overview of CNNs.vtt 6KB
  133. 03. Setting up the working environment/2. Why Python and why Jupyter.vtt 6KB
  134. 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.vtt 6KB
  135. 01. Welcome! Course introduction/2. What does the course cover.vtt 5KB
  136. 13. Business case/8. Learning and interpreting the result.vtt 5KB
  137. 05. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.vtt 5KB
  138. 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.vtt 5KB
  139. 11. Preprocessing/3. Standardization.vtt 5KB
  140. 10. Gradient descent and learning rates/4. Learning rate schedules.vtt 5KB
  141. 12. The MNIST example/13. Testing the model.vtt 5KB
  142. 02. Introduction to neural networks/1. Introduction to neural networks.vtt 5KB
  143. 08. Overfitting/1. Underfitting and overfitting.vtt 5KB
  144. 02. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.vtt 5KB
  145. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.vtt 5KB
  146. 02. Introduction to neural networks/5. Types of machine learning.vtt 5KB
  147. 02. Introduction to neural networks/20. Cross-entropy loss.vtt 5KB
  148. 05. TensorFlow - An introduction/1. TensorFlow outline.vtt 5KB
  149. 15. Conclusion/1. See how much you have learned.vtt 5KB
  150. 15. Conclusion/6. An overview of non-NN approaches.vtt 5KB
  151. 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.vtt 5KB
  152. 06. Going deeper Introduction to deep neural networks/5. Activation functions.vtt 5KB
  153. 08. Overfitting/3. Training and validation.vtt 4KB
  154. 10. Gradient descent and learning rates/1. Stochastic gradient descent.vtt 4KB
  155. 11. Preprocessing/5. One-hot and binary encoding.vtt 4KB
  156. 13. Business case/6. Load the preprocessed data.vtt 4KB
  157. 03. Setting up the working environment/4. Installing Anaconda.vtt 4KB
  158. 13. Business case/3. Balancing the dataset.vtt 4KB
  159. 04. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.vtt 4KB
  160. 04. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.vtt 4KB
  161. 06. Going deeper Introduction to deep neural networks/7. Backpropagation.vtt 4KB
  162. 02. Introduction to neural networks/3. Training the model.vtt 4KB
  163. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.vtt 4KB
  164. 06. Going deeper Introduction to deep neural networks/6. Softmax activation.vtt 4KB
  165. 08. Overfitting/5. N-fold cross validation.vtt 4KB
  166. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.vtt 4KB
  167. 05. TensorFlow - An introduction/7. Cutomizing your model.vtt 4KB
  168. 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.vtt 4KB
  169. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.vtt 3KB
  170. 02. Introduction to neural networks/7. The linear model.vtt 3KB
  171. 06. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.vtt 3KB
  172. 11. Preprocessing/1. Preprocessing introduction.vtt 3KB
  173. 06. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.vtt 3KB
  174. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.vtt 3KB
  175. 09. Initialization/3. Xavier initialization.vtt 3KB
  176. 15. Conclusion/5. An overview of RNNs.vtt 3KB
  177. 09. Initialization/2. Types of simple initializations.vtt 3KB
  178. 05. TensorFlow - An introduction/2. TensorFlow 2 intro.vtt 3KB
  179. 14. Appendix Linear Algebra Fundamentals/5. Tensors.vtt 3KB
  180. 12. The MNIST example/1. The dataset.vtt 3KB
  181. 09. Initialization/1. Initialization - Introduction.vtt 3KB
  182. 12. The MNIST example/2. How to tackle the MNIST.vtt 3KB
  183. 10. Gradient descent and learning rates/3. Momentum.vtt 3KB
  184. 08. Overfitting/4. Training, validation, and test.vtt 3KB
  185. 05. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.vtt 3KB
  186. 10. Gradient descent and learning rates/7. Adaptive moment estimation.vtt 3KB
  187. 06. Going deeper Introduction to deep neural networks/2. What is a deep net.vtt 3KB
  188. 03. Setting up the working environment/5. The Jupyter dashboard - part 1.vtt 3KB
  189. 02. Introduction to neural networks/10. The linear model. Multiple inputs.vtt 3KB
  190. 12. The MNIST example/3. Importing the relevant packages and load the data.vtt 3KB
  191. 12. The MNIST example/9. Select the loss and the optimizer.vtt 3KB
  192. 16. Bonus lecture/1. Bonus lecture Next steps.html 3KB
  193. 10. Gradient descent and learning rates/2. Gradient descent pitfalls.vtt 3KB
  194. 02. Introduction to neural networks/18. L2-norm loss.vtt 2KB
  195. 11. Preprocessing/4. Dealing with categorical data.vtt 2KB
  196. 08. Overfitting/2. Underfitting and overfitting - classification.vtt 2KB
  197. 02. Introduction to neural networks/14. Graphical representation.vtt 2KB
  198. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.vtt 2KB
  199. 15. Conclusion/2. What’s further out there in the machine and deep learning world.vtt 2KB
  200. 06. Going deeper Introduction to deep neural networks/1. Layers.vtt 2KB
  201. 12. The MNIST example/12. MNIST - solutions.html 2KB
  202. 12. The MNIST example/11. MNIST - exercises.html 2KB
  203. 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.vtt 2KB
  204. 02. Introduction to neural networks/16. The objective function.vtt 2KB
  205. 13. Business case/11. Testing the model.vtt 2KB
  206. 13. Business case/2. Outlining the business case solution.vtt 2KB
  207. 04. Minimal example - your first machine learning algorithm/5. Minimal example - Exercises.html 2KB
  208. 11. Preprocessing/2. Basic preprocessing.vtt 1KB
  209. 15. Conclusion/4. How DeepMind uses deep learning.html 1KB
  210. 05. TensorFlow - An introduction/8. Minimal example - Exercises.html 1KB
  211. 05. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.vtt 1KB
  212. 03. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.vtt 1KB
  213. 02. Introduction to neural networks/9. Need Help with Linear Algebra.html 829B
  214. 07. Backpropagation. A peek into the Mathematics of Optimization/1. Backpropagation. A peek into the Mathematics of Optimization.html 539B
  215. 13. Business case/12. Final exercise.html 445B
  216. 13. Business case/5. Preprocessing exercise.html 404B
  217. 03. Setting up the working environment/11. Installing packages - solution.html 339B
  218. 03. Setting up the working environment/7. Jupyter Shortcuts.html 332B
  219. 03. Setting up the working environment/10. Installing packages - exercise.html 227B
  220. 14. Appendix Linear Algebra Fundamentals/7.1 Errors when Adding Matrices Python Notebook.html 220B
  221. 13. Business case/10. Setting an early stopping mechanism - Exercise.html 191B
  222. 14. Appendix Linear Algebra Fundamentals/4.1 Scalars, Vectors and Matrices Python Notebook.html 181B
  223. 14. Appendix Linear Algebra Fundamentals/6.1 Addition and Subtraction Python Notebook.html 178B
  224. 12. The MNIST example/12.1 4. TensorFlow MNIST - Exercise 4 Solution.html 172B
  225. 12. The MNIST example/12.3 5. TensorFlow MNIST - Exercise 5 Solution.html 172B
  226. 13. Business case/7.1 TensorFlow Business Case - Machine Learning - Part 1.html 172B
  227. 13. Business case/8.1 TensorFlow Business Case - Machine Learning - Part 2.html 172B
  228. 13. Business case/9.1 TensorFlow Business Case - Machine Learning - Part 3.html 172B
  229. 14. Appendix Linear Algebra Fundamentals/10.1 Dot Product of Matrices Python Notebook.html 171B
  230. 13. Business case/5.1 TensorFlow Business Case - Preprocessing Exercise Solution.html 167B
  231. 14. Appendix Linear Algebra Fundamentals/8.1 Transpose of a Matrix Python Notebook.html 167B
  232. 13. Business case/11.1 TensorFlow Business Case - Machine Learning Complete Code with Comments.html 166B
  233. 13. Business case/12.1 TensorFlow Business Case - Machine Learning Complete Code with Comments.html 166B
  234. 12. The MNIST example/12.5 8. TensorFlow MNIST - Exercise 8 Solution.html 165B
  235. 12. The MNIST example/12.9 9. TensorFlow MNIST - Exercise 9 Solution.html 165B
  236. 05. TensorFlow - An introduction/7.1 TensorFlow Minimal Example - Complete Code with Comments.html 163B
  237. 13. Business case/4.1 TensorFlow Business Case - Preprocessing with Comments.html 163B
  238. 05. TensorFlow - An introduction/8.3 TensorFlow Minimal Example - Exercise 2_1 - Solution.html 162B
  239. 05. TensorFlow - An introduction/8.5 TensorFlow Minimal Example - Exercise 2_2 - Solution.html 162B
  240. 12. The MNIST example/12.2 7. TensorFlow MNIST - Exercise 7 Solution.html 162B
  241. 12. The MNIST example/12.8 6. TensorFlow MNIST - Exercise 6 Solution.html 162B
  242. 01. Welcome! Course introduction/3. What does the course cover - Quiz.html 161B
  243. 02. Introduction to neural networks/2. Introduction to neural networks - Quiz.html 161B
  244. 02. Introduction to neural networks/4. Training the model - Quiz.html 161B
  245. 02. Introduction to neural networks/6. Types of machine learning - Quiz.html 161B
  246. 02. Introduction to neural networks/8. The linear model - Quiz.html 161B
  247. 02. Introduction to neural networks/11. The linear model. Multiple inputs - Quiz.html 161B
  248. 02. Introduction to neural networks/13. The linear model. Multiple inputs and multiple outputs - Quiz.html 161B
  249. 02. Introduction to neural networks/15. Graphical representation - Quiz.html 161B
  250. 02. Introduction to neural networks/17. The objective function - Quiz.html 161B
  251. 02. Introduction to neural networks/19. L2-norm loss - Quiz.html 161B
  252. 02. Introduction to neural networks/21. Cross-entropy loss - Quiz.html 161B
  253. 02. Introduction to neural networks/23. One parameter gradient descent - Quiz.html 161B
  254. 02. Introduction to neural networks/25. N-parameter gradient descent - Quiz.html 161B
  255. 03. Setting up the working environment/3. Why Python and why Jupyter - Quiz.html 161B
  256. 03. Setting up the working environment/8. The Jupyter dashboard - Quiz.html 161B
  257. 05. TensorFlow - An introduction/8.1 TensorFlow Minimal Example - Exercise 3 - Solution.html 160B
  258. 05. TensorFlow - An introduction/8.2 TensorFlow Minimal Example - Exercise 1 - Solution.html 160B
  259. 12. The MNIST example/12.10 3. TensorFlow MNIST - Exercise 3 Solution.html 160B
  260. 13. Business case/5.3 TensorFlow Business Case - Preprocessing Exercise.html 158B
  261. 12. The MNIST example/12.7 10. TensorFlow MNIST - Exercise 10 Solution.html 157B
  262. 04. Minimal example - your first machine learning algorithm/5.7 Minimal_example_Exercise_3.d. Solution.html 154B
  263. 04. Minimal example - your first machine learning algorithm/5.8 Minimal_example_Exercise_3.b. Solution.html 154B
  264. 04. Minimal example - your first machine learning algorithm/5.9 Minimal_example_Exercise_3.a. Solution.html 154B
  265. 04. Minimal example - your first machine learning algorithm/5.10 Minimal_example_Exercise_3.c. Solution.html 154B
  266. 05. TensorFlow - An introduction/8.4 TensorFlow Minimal Example - All Exercises.html 154B
  267. 14. Appendix Linear Algebra Fundamentals/9.1 Dot Product Python Notebook.html 154B
  268. 12. The MNIST example/13.1 TensorFlow MNIST - Complete Code with Comments.html 153B
  269. 12. The MNIST example/3.1 TensorFlow MNIST - Part 1 with comments.html 150B
  270. 12. The MNIST example/5.1 TensorFlow MNIST - Part 2 with comments.html 150B
  271. 12. The MNIST example/7.1 TensorFlow MNIST - Part 3 with comments.html 150B
  272. 12. The MNIST example/8.1 TensorFlow MNIST - Part 4 with comments.html 150B
  273. 12. The MNIST example/9.1 TensorFlow MNIST - Part 5 with comments.html 150B
  274. 12. The MNIST example/10.1 TensorFlow MNIST - Part 6 with comments.html 150B
  275. 12. The MNIST example/12.4 1. TensorFlow MNIST - Exercise 1 Solution.html 150B
  276. 12. The MNIST example/12.6 2. TensorFlow MNIST - Exercise 2 Solution.html 150B
  277. 04. Minimal example - your first machine learning algorithm/5.2 Minimal_example_Exercise_1_Solution.html 149B
  278. 04. Minimal example - your first machine learning algorithm/5.3 Minimal_example_Exercise_5_Solution.html 149B
  279. 04. Minimal example - your first machine learning algorithm/5.4 Minimal_example_Exercise_2_Solution.html 149B
  280. 04. Minimal example - your first machine learning algorithm/5.5 Minimal_example_Exercise_4_Solution.html 149B
  281. 04. Minimal example - your first machine learning algorithm/5.6 Minimal_example_Exercise_6_Solution.html 149B
  282. 05. TensorFlow - An introduction/7.2 TensorFlow Minimal Example - Complete Code.html 149B
  283. 13. Business case/4.2 TensorFlow Business Case - Preprocessing.html 149B
  284. 14. Appendix Linear Algebra Fundamentals/5.1 Tensors Notebook.html 148B
  285. 05. TensorFlow - An introduction/4.1 TensorFlow Minimal Example - Part 1.html 146B
  286. 05. TensorFlow - An introduction/5.1 TensorFlow Minimal Example - Part 2.html 146B
  287. 05. TensorFlow - An introduction/6.1 TensorFlow Minimal Example - Part 3.html 146B
  288. 04. Minimal example - your first machine learning algorithm/4.1 Minimal example - part 4.html 145B
  289. 12. The MNIST example/11.1 TensorFlow MNIST - All Exercises.html 144B
  290. 04. Minimal example - your first machine learning algorithm/5.1 Minimal_example_All_Exercises.html 143B
  291. 12. The MNIST example/13.2 TensorFlow MNIST - Complete Code.html 139B
  292. 04. Minimal example - your first machine learning algorithm/1.1 Minimal example Part 1.html 136B
  293. 04. Minimal example - your first machine learning algorithm/2.1 Minimal example - part 2.html 136B
  294. 04. Minimal example - your first machine learning algorithm/3.1 Minimal example - part 3.html 136B
  295. udemycoursedownloader.com.url 132B
  296. Udemy Course downloader.txt 94B
  297. 12. The MNIST example/5. Preprocess the data - scale the test data.html 81B
  298. 12. The MNIST example/7. Preprocess the data - shuffle and batch the data.html 81B
  299. 13. Business case/7. Load the preprocessed data - Exercise.html 79B