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[] Udemy - Deep Learning with TensorFlow 2.0 [2020]

  • 收录时间:2020-07-31 14:20:39
  • 文件大小:2GB
  • 下载次数:10
  • 最近下载:2020-12-08 05:35:12
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文件列表

  1. 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4 144MB
  2. 1. 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 84MB
  4. 13. Business case/1. Exploring the dataset and identifying predictors.mp4 66MB
  5. 13. Business case/9. Setting an early stopping mechanism.mp4 50MB
  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 42MB
  9. 12. The MNIST example/10. Learning.mp4 41MB
  10. 2. Introduction to neural networks/24. N-parameter gradient descent.mp4 39MB
  11. 3. Setting up the working environment/9. Installing TensorFlow 2.mp4 39MB
  12. 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 38MB
  13. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4 38MB
  14. 5. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 35MB
  15. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4 34MB
  16. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 34MB
  17. 5. TensorFlow - An introduction/1. TensorFlow outline.mp4 34MB
  18. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 33MB
  19. 3. Setting up the working environment/2. Why Python and why Jupyter.mp4 32MB
  20. 13. Business case/8. Learning and interpreting the result.mp4 31MB
  21. 13. Business case/3. Balancing the dataset.mp4 30MB
  22. 5. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp4 30MB
  23. 12. The MNIST example/13. Testing the model.mp4 30MB
  24. 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp4 29MB
  25. 3. Setting up the working environment/4. Installing Anaconda.mp4 28MB
  26. 12. The MNIST example/8. Outline the model.mp4 28MB
  27. 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4 27MB
  28. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 24MB
  29. 5. TensorFlow - An introduction/7. Cutomizing your model.mp4 23MB
  30. 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 23MB
  31. 5. TensorFlow - An introduction/2. TensorFlow 2 intro.mp4 22MB
  32. 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 21MB
  33. 3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 19MB
  34. 12. The MNIST example/2. How to tackle the MNIST.mp4 19MB
  35. 2. Introduction to neural networks/22. One parameter gradient descent.mp4 18MB
  36. 13. Business case/6. Load the preprocessed data.mp4 18MB
  37. 5. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp4 16MB
  38. 1. Welcome! Course introduction/2. What does the course cover.mp4 16MB
  39. 12. The MNIST example/3. Importing the relevant packages and load the data.mp4 16MB
  40. 15. Conclusion/1. See how much you have learned.mp4 14MB
  41. 12. The MNIST example/9. Select the loss and the optimizer.mp4 14MB
  42. 2. Introduction to neural networks/1. Introduction to neural networks.mp4 14MB
  43. 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 13MB
  44. 12. The MNIST example/1. The dataset.mp4 13MB
  45. 2. Introduction to neural networks/5. Types of machine learning.mp4 12MB
  46. 2. Introduction to neural networks/20. Cross-entropy loss.mp4 11MB
  47. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4 11MB
  48. 8. Overfitting/1. Underfitting and overfitting.mp4 11MB
  49. 6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11MB
  50. 15. Conclusion/3. An overview of CNNs.mp4 11MB
  51. 13. Business case/11. Testing the model.mp4 11MB
  52. 4. 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. 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4 10MB
  55. 8. Overfitting/6. Early stopping.mp4 9MB
  56. 10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4 9MB
  57. 8. Overfitting/3. Training and validation.mp4 9MB
  58. 2. Introduction to neural networks/7. The linear model.mp4 9MB
  59. 6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4 9MB
  60. 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp4 9MB
  61. 2. Introduction to neural networks/3. Training the model.mp4 9MB
  62. 6. Going deeper Introduction to deep neural networks/5. Activation functions.mp4 9MB
  63. 3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 9MB
  64. 11. Preprocessing/1. Preprocessing introduction.mp4 8MB
  65. 11. Preprocessing/3. Standardization.mp4 8MB
  66. 9. Initialization/1. Initialization - Introduction.mp4 8MB
  67. 15. Conclusion/6. An overview of non-NN approaches.mp4 8MB
  68. 10. Gradient descent and learning rates/7. Adaptive moment estimation.mp4 8MB
  69. 2. Introduction to neural networks/10. The linear model. Multiple inputs.mp4 8MB
  70. 8. Overfitting/4. Training, validation, and test.mp4 7MB
  71. 6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7MB
  72. 13. Business case/2. Outlining the business case solution.mp4 7MB
  73. 2. Introduction to neural networks/18. L2-norm loss.mp4 7MB
  74. 8. Overfitting/5. N-fold cross validation.mp4 7MB
  75. 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4 7MB
  76. 8. Overfitting/2. Underfitting and overfitting - classification.mp4 7MB
  77. 5. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp4 7MB
  78. 6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 7MB
  79. 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 7MB
  80. 2. Introduction to neural networks/14. Graphical representation.mp4 6MB
  81. 15. Conclusion/2. What’s further out there in the machine and deep learning world.mp4 6MB
  82. 11. Preprocessing/5. One-hot and binary encoding.mp4 6MB
  83. 10. Gradient descent and learning rates/3. Momentum.mp4 6MB
  84. 11. Preprocessing/4. Dealing with categorical data.mp4 6MB
  85. 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 6MB
  86. 9. Initialization/3. Xavier initialization.mp4 6MB
  87. 2. Introduction to neural networks/16. The objective function.mp4 6MB
  88. 9. Initialization/2. Types of simple initializations.mp4 6MB
  89. 15. Conclusion/5. An overview of RNNs.mp4 5MB
  90. 6. 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. 6. Going deeper Introduction to deep neural networks/1.1 Course Notes - Section 6.pdf 936KB
  95. 6. Going deeper Introduction to deep neural networks/2.1 Course Notes - Section 6.pdf 936KB
  96. 2. Introduction to neural networks/1.1 Course Notes - Section 2.pdf 928KB
  97. 2. Introduction to neural networks/10.1 Course Notes - Section 2.pdf 928KB
  98. 2. Introduction to neural networks/12.1 Course Notes - Section 2.pdf 928KB
  99. 2. Introduction to neural networks/14.1 Course Notes - Section 2.pdf 928KB
  100. 2. Introduction to neural networks/16.1 Course Notes - Section 2.pdf 928KB
  101. 2. Introduction to neural networks/18.1 Course Notes - Section 2.pdf 928KB
  102. 2. Introduction to neural networks/20.1 Course Notes - Section 2.pdf 928KB
  103. 2. Introduction to neural networks/22.1 Course Notes - Section 2.pdf 928KB
  104. 2. Introduction to neural networks/24.1 Course Notes - Section 2.pdf 928KB
  105. 2. Introduction to neural networks/3.1 Course Notes - Section 2.pdf 928KB
  106. 2. Introduction to neural networks/5.1 Course Notes - Section 2.pdf 928KB
  107. 2. Introduction to neural networks/7.1 Course Notes - Section 2.pdf 928KB
  108. 13. Business case/1.1 Audiobooks_data.csv 625KB
  109. 13. Business case/4.3 Audiobooks_data.csv 625KB
  110. 13. Business case/5.3 Audiobooks_data.csv 625KB
  111. 3. Setting up the working environment/7.1 Shortcuts for Jupyter.pdf 619KB
  112. 7. Backpropagation. A peek into the Mathematics of Optimization/1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182KB
  113. 2. Introduction to neural networks/22.2 GD-function-example.xlsx 42KB
  114. 13. Business case/4. Preprocessing the data.srt 12KB
  115. 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.srt 12KB
  116. 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.srt 11KB
  117. 13. Business case/1. Exploring the dataset and identifying predictors.srt 11KB
  118. 1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.srt 10KB
  119. 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.srt 10KB
  120. 12. The MNIST example/6. Preprocess the data - shuffle and batch the data.srt 9KB
  121. 2. Introduction to neural networks/22. One parameter gradient descent.srt 8KB
  122. 12. The MNIST example/10. Learning.srt 8KB
  123. 5. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.srt 8KB
  124. 13. Business case/9. Setting an early stopping mechanism.srt 8KB
  125. 2. Introduction to neural networks/24. N-parameter gradient descent.srt 8KB
  126. 12. The MNIST example/8. Outline the model.srt 7KB
  127. 8. Overfitting/6. Early stopping.srt 7KB
  128. 4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.srt 7KB
  129. 3. Setting up the working environment/6. The Jupyter dashboard - part 2.srt 7KB
  130. 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.srt 7KB
  131. 15. Conclusion/3. An overview of CNNs.srt 6KB
  132. 3. Setting up the working environment/9. Installing TensorFlow 2.srt 6KB
  133. 3. Setting up the working environment/2. Why Python and why Jupyter.srt 6KB
  134. 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.srt 6KB
  135. 13. Business case/8. Learning and interpreting the result.srt 6KB
  136. 1. Welcome! Course introduction/2. What does the course cover.srt 6KB
  137. 5. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.srt 6KB
  138. 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.srt 6KB
  139. 12. The MNIST example/13. Testing the model.srt 6KB
  140. 10. Gradient descent and learning rates/4. Learning rate schedules.srt 6KB
  141. 11. Preprocessing/3. Standardization.srt 6KB
  142. 2. Introduction to neural networks/1. Introduction to neural networks.srt 6KB
  143. 8. Overfitting/1. Underfitting and overfitting.srt 6KB
  144. 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.srt 5KB
  145. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.srt 5KB
  146. 2. Introduction to neural networks/20. Cross-entropy loss.srt 5KB
  147. 2. Introduction to neural networks/5. Types of machine learning.srt 5KB
  148. 5. TensorFlow - An introduction/1. TensorFlow outline.srt 5KB
  149. 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.srt 5KB
  150. 15. Conclusion/1. See how much you have learned.srt 5KB
  151. 6. Going deeper Introduction to deep neural networks/5. Activation functions.srt 5KB
  152. 15. Conclusion/6. An overview of non-NN approaches.srt 5KB
  153. 10. Gradient descent and learning rates/1. Stochastic gradient descent.srt 5KB
  154. 8. Overfitting/3. Training and validation.srt 5KB
  155. 11. Preprocessing/5. One-hot and binary encoding.srt 5KB
  156. 13. Business case/6. Load the preprocessed data.srt 5KB
  157. 3. Setting up the working environment/4. Installing Anaconda.srt 5KB
  158. 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.srt 5KB
  159. 13. Business case/3. Balancing the dataset.srt 4KB
  160. 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.srt 4KB
  161. 6. Going deeper Introduction to deep neural networks/7. Backpropagation.srt 4KB
  162. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.srt 4KB
  163. 6. Going deeper Introduction to deep neural networks/6. Softmax activation.srt 4KB
  164. 2. Introduction to neural networks/3. Training the model.srt 4KB
  165. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.srt 4KB
  166. 8. Overfitting/5. N-fold cross validation.srt 4KB
  167. 5. TensorFlow - An introduction/7. Cutomizing your model.srt 4KB
  168. 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.srt 4KB
  169. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.srt 4KB
  170. 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.srt 4KB
  171. 2. Introduction to neural networks/7. The linear model.srt 4KB
  172. 11. Preprocessing/1. Preprocessing introduction.srt 4KB
  173. 6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.srt 4KB
  174. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.srt 4KB
  175. 9. Initialization/3. Xavier initialization.srt 4KB
  176. 9. Initialization/2. Types of simple initializations.srt 4KB
  177. 5. TensorFlow - An introduction/2. TensorFlow 2 intro.srt 4KB
  178. 15. Conclusion/5. An overview of RNNs.srt 4KB
  179. 14. Appendix Linear Algebra Fundamentals/5. Tensors.srt 4KB
  180. 12. The MNIST example/1. The dataset.srt 4KB
  181. 8. Overfitting/4. Training, validation, and test.srt 4KB
  182. 9. Initialization/1. Initialization - Introduction.srt 4KB
  183. 10. Gradient descent and learning rates/3. Momentum.srt 4KB
  184. 12. The MNIST example/2. How to tackle the MNIST.srt 4KB
  185. 5. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.srt 4KB
  186. 10. Gradient descent and learning rates/7. Adaptive moment estimation.srt 3KB
  187. 6. Going deeper Introduction to deep neural networks/2. What is a deep net.srt 3KB
  188. 3. Setting up the working environment/5. The Jupyter dashboard - part 1.srt 3KB
  189. 2. Introduction to neural networks/10. The linear model. Multiple inputs.srt 3KB
  190. 12. The MNIST example/3. Importing the relevant packages and load the data.srt 3KB
  191. 12. The MNIST example/9. Select the loss and the optimizer.srt 3KB
  192. 10. Gradient descent and learning rates/2. Gradient descent pitfalls.srt 3KB
  193. 2. Introduction to neural networks/18. L2-norm loss.srt 3KB
  194. 11. Preprocessing/4. Dealing with categorical data.srt 3KB
  195. 8. Overfitting/2. Underfitting and overfitting - classification.srt 3KB
  196. 2. Introduction to neural networks/14. Graphical representation.srt 3KB
  197. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.srt 3KB
  198. 15. Conclusion/2. What’s further out there in the machine and deep learning world.srt 3KB
  199. 16. Bonus lecture/1. Bonus lecture Next steps.html 3KB
  200. 6. Going deeper Introduction to deep neural networks/1. Layers.srt 2KB
  201. 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.srt 2KB
  202. 12. The MNIST example/12. MNIST - solutions.html 2KB
  203. 13. Business case/11. Testing the model.srt 2KB
  204. 2. Introduction to neural networks/16. The objective function.srt 2KB
  205. 13. Business case/2. Outlining the business case solution.srt 2KB
  206. 12. The MNIST example/11. MNIST - exercises.html 2KB
  207. 11. Preprocessing/2. Basic preprocessing.srt 2KB
  208. 4. Minimal example - your first machine learning algorithm/5. Minimal example - Exercises.html 2KB
  209. 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.srt 1KB
  210. 5. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.srt 1KB
  211. 15. Conclusion/4. How DeepMind uses deep learning.html 1KB
  212. 5. TensorFlow - An introduction/8. Minimal example with TensorFlow - Exercises.html 1KB
  213. 2. Introduction to neural networks/9. Need Help with Linear Algebra.html 829B
  214. 1. Welcome! Course introduction/4. Download All Resources and Important FAQ.html 720B
  215. 7. Backpropagation. A peek into the Mathematics of Optimization/1. Backpropagation. A peek into the Mathematics of Optimization.html 539B
  216. 13. Business case/12. Final exercise.html 445B
  217. 13. Business case/5. Preprocessing exercise.html 404B
  218. 3. Setting up the working environment/7. Jupyter Shortcuts.html 332B
  219. 3. Setting up the working environment/11. Installing packages - solution.html 267B
  220. 14. Appendix Linear Algebra Fundamentals/7.1 Errors when Adding Matrices Python Notebook.html 220B
  221. 3. Setting up the working environment/10. Installing packages - exercise.html 198B
  222. 13. Business case/10. Setting an early stopping mechanism - Exercise.html 191B
  223. 14. Appendix Linear Algebra Fundamentals/4.1 Scalars, Vectors and Matrices Python Notebook.html 181B
  224. 14. Appendix Linear Algebra Fundamentals/6.1 Addition and Subtraction Python Notebook.html 178B
  225. 12. The MNIST example/12.4 4. TensorFlow MNIST - Exercise 4 Solution.html 172B
  226. 12. The MNIST example/12.5 5. TensorFlow MNIST - Exercise 5 Solution.html 172B
  227. 13. Business case/7.1 TensorFlow Business Case - Machine Learning - Part 1.html 172B
  228. 13. Business case/8.1 TensorFlow Business Case - Machine Learning - Part 2.html 172B
  229. 13. Business case/9.1 TensorFlow Business Case - Machine Learning - Part 3.html 172B
  230. 14. Appendix Linear Algebra Fundamentals/10.1 Dot Product of Matrices Python Notebook.html 171B
  231. 1. Welcome! Course introduction/3. What does the course cover - Quiz.html 168B
  232. 2. Introduction to neural networks/11. The linear model. Multiple inputs - Quiz.html 168B
  233. 2. Introduction to neural networks/13. The linear model. Multiple inputs and multiple outputs - Quiz.html 168B
  234. 2. Introduction to neural networks/15. Graphical representation - Quiz.html 168B
  235. 2. Introduction to neural networks/17. The objective function - Quiz.html 168B
  236. 2. Introduction to neural networks/19. L2-norm loss - Quiz.html 168B
  237. 2. Introduction to neural networks/2. Introduction to neural networks - Quiz.html 168B
  238. 2. Introduction to neural networks/21. Cross-entropy loss - Quiz.html 168B
  239. 2. Introduction to neural networks/23. One parameter gradient descent - Quiz.html 168B
  240. 2. Introduction to neural networks/25. N-parameter gradient descent - Quiz.html 168B
  241. 2. Introduction to neural networks/4. Training the model - Quiz.html 168B
  242. 2. Introduction to neural networks/6. Types of machine learning - Quiz.html 168B
  243. 2. Introduction to neural networks/8. The linear model - Quiz.html 168B
  244. 3. Setting up the working environment/3. Why Python and why Jupyter - Quiz.html 168B
  245. 3. Setting up the working environment/8. The Jupyter dashboard - Quiz.html 168B
  246. 13. Business case/5.2 TensorFlow Business Case - Preprocessing Exercise Solution.html 167B
  247. 14. Appendix Linear Algebra Fundamentals/8.1 Transpose of a Matrix Python Notebook.html 167B
  248. 13. Business case/11.1 TensorFlow Business Case - Machine Learning Complete Code with Comments.html 166B
  249. 13. Business case/12.1 TensorFlow Business Case - Machine Learning Complete Code with Comments.html 166B
  250. 12. The MNIST example/12.6 8. TensorFlow MNIST - Exercise 8 Solution.html 165B
  251. 12. The MNIST example/12.8 9. TensorFlow MNIST - Exercise 9 Solution.html 165B
  252. 13. Business case/4.2 TensorFlow Business Case - Preprocessing with Comments.html 163B
  253. 5. TensorFlow - An introduction/7.1 TensorFlow Minimal Example - Complete Code with Comments.html 163B
  254. 12. The MNIST example/12.10 7. TensorFlow MNIST - Exercise 7 Solution.html 162B
  255. 12. The MNIST example/12.9 6. TensorFlow MNIST - Exercise 6 Solution.html 162B
  256. 5. TensorFlow - An introduction/8.1 TensorFlow Minimal Example - Exercise 2_2 - Solution.html 162B
  257. 5. TensorFlow - An introduction/8.4 TensorFlow Minimal Example - Exercise 2_1 - Solution.html 162B
  258. 12. The MNIST example/12.1 3. TensorFlow MNIST - Exercise 3 Solution.html 160B
  259. 5. TensorFlow - An introduction/8.2 TensorFlow Minimal Example - Exercise 1 - Solution.html 160B
  260. 5. TensorFlow - An introduction/8.3 TensorFlow Minimal Example - Exercise 3 - Solution.html 160B
  261. 13. Business case/5.1 TensorFlow Business Case - Preprocessing Exercise.html 158B
  262. 12. The MNIST example/12.7 10. TensorFlow MNIST - Exercise 10 Solution.html 157B
  263. 14. Appendix Linear Algebra Fundamentals/9.1 Dot Product Python Notebook.html 154B
  264. 4. Minimal example - your first machine learning algorithm/5.2 Minimal_example_Exercise_3.c. Solution.html 154B
  265. 4. Minimal example - your first machine learning algorithm/5.4 Minimal_example_Exercise_3.a. Solution.html 154B
  266. 4. Minimal example - your first machine learning algorithm/5.7 Minimal_example_Exercise_3.d. Solution.html 154B
  267. 4. Minimal example - your first machine learning algorithm/5.8 Minimal_example_Exercise_3.b. Solution.html 154B
  268. 5. TensorFlow - An introduction/8.5 TensorFlow Minimal Example - All Exercises.html 154B
  269. 12. The MNIST example/13.2 TensorFlow MNIST - Complete Code with Comments.html 153B
  270. 12. The MNIST example/10.1 TensorFlow MNIST - Part 6 with comments.html 150B
  271. 12. The MNIST example/12.2 1. TensorFlow MNIST - Exercise 1 Solution.html 150B
  272. 12. The MNIST example/12.3 2. TensorFlow MNIST - Exercise 2 Solution.html 150B
  273. 12. The MNIST example/3.1 TensorFlow MNIST - Part 1 with comments.html 150B
  274. 12. The MNIST example/5.1 TensorFlow MNIST - Part 2 with comments.html 150B
  275. 12. The MNIST example/7.1 TensorFlow MNIST - Part 3 with comments.html 150B
  276. 12. The MNIST example/8.1 TensorFlow MNIST - Part 4 with comments.html 150B
  277. 12. The MNIST example/9.1 TensorFlow MNIST - Part 5 with comments.html 150B
  278. 13. Business case/4.1 TensorFlow Business Case - Preprocessing.html 149B
  279. 4. Minimal example - your first machine learning algorithm/5.1 Minimal_example_Exercise_6_Solution.html 149B
  280. 4. Minimal example - your first machine learning algorithm/5.3 Minimal_example_Exercise_4_Solution.html 149B
  281. 4. Minimal example - your first machine learning algorithm/5.5 Minimal_example_Exercise_5_Solution.html 149B
  282. 4. Minimal example - your first machine learning algorithm/5.6 Minimal_example_Exercise_1_Solution.html 149B
  283. 4. Minimal example - your first machine learning algorithm/5.9 Minimal_example_Exercise_2_Solution.html 149B
  284. 5. TensorFlow - An introduction/7.2 TensorFlow Minimal Example - Complete Code.html 149B
  285. 14. Appendix Linear Algebra Fundamentals/5.1 Tensors Notebook.html 148B
  286. 5. TensorFlow - An introduction/4.1 TensorFlow Minimal Example - Part 1.html 146B
  287. 5. TensorFlow - An introduction/5.1 TensorFlow Minimal Example - Part 2.html 146B
  288. 5. TensorFlow - An introduction/6.1 TensorFlow Minimal Example - Part 3.html 146B
  289. 4. Minimal example - your first machine learning algorithm/4.1 Minimal example - part 4.html 145B
  290. 12. The MNIST example/11.1 TensorFlow MNIST - All Exercises.html 144B
  291. 4. Minimal example - your first machine learning algorithm/5.10 Minimal_example_All_Exercises.html 143B
  292. 12. The MNIST example/13.1 TensorFlow MNIST - Complete Code.html 139B
  293. 4. Minimal example - your first machine learning algorithm/1.1 Minimal example Part 1.html 136B
  294. 4. Minimal example - your first machine learning algorithm/2.1 Minimal example - part 2.html 136B
  295. 4. Minimal example - your first machine learning algorithm/3.1 Minimal example - part 3.html 136B
  296. [Tutorialsplanet.NET].url 128B
  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