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[] Udemy - Master Deep Learning with TensorFlow in Python

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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. 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp4 50MB
  4. 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4 49MB
  5. 2. Introduction to neural networks/24. N-parameter gradient descent.mp4 39MB
  6. 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 38MB
  7. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4 38MB
  8. 13. Business case/4. Preprocessing the data.mp4 34MB
  9. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4 34MB
  10. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 34MB
  11. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 33MB
  12. 13. Business case/6. Create a class for batching.mp4 28MB
  13. 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4 27MB
  14. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 24MB
  15. 13. Business case/1. Exploring the dataset and identifying predictors.mp4 23MB
  16. 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 23MB
  17. 12. The MNIST example/9. Discuss the results and test.mp4 22MB
  18. 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 21MB
  19. 13. Business case/7. Outlining the model.mp4 19MB
  20. 12. The MNIST example/4. Outlining the model.mp4 18MB
  21. 2. Introduction to neural networks/22. One parameter gradient descent.mp4 18MB
  22. 1. Welcome! Course introduction/2. What does the course cover.mp4 16MB
  23. 12. The MNIST example/8. Learning.mp4 16MB
  24. 5. TensorFlow - An introduction/1. TensorFlow outline.mp4 14MB
  25. 5. TensorFlow - An introduction/6. Model output.mp4 14MB
  26. 15. Conclusion/1. See how much you have learned.mp4 14MB
  27. 13. Business case/3. Balancing the dataset.mp4 14MB
  28. 3. Setting up the working environment/2. Why Python and why Jupyter.mp4 14MB
  29. 2. Introduction to neural networks/1. Introduction to neural networks.mp4 14MB
  30. 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 13MB
  31. 13. Business case/11. A comment on the homework.mp4 13MB
  32. 5. TensorFlow - An introduction/4. Inputs, outputs, targets, weights, biases - model layout.mp4 13MB
  33. 12. The MNIST example/6. Accuracy of prediction.mp4 12MB
  34. 13. Business case/8. Optimizing the algorithm.mp4 12MB
  35. 2. Introduction to neural networks/5. Types of machine learning.mp4 12MB
  36. 2. Introduction to neural networks/20. Cross-entropy loss.mp4 11MB
  37. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4 11MB
  38. 6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11MB
  39. 8. Overfitting/1. Underfitting and overfitting.mp4 11MB
  40. 15. Conclusion/3. An overview of CNNs.mp4 11MB
  41. 3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 11MB
  42. 4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp4 11MB
  43. 10. Gradient descent and learning rates/4. Learning rate schedules.mp4 10MB
  44. 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4 10MB
  45. 5. TensorFlow - An introduction/5. Loss function and gradient descent - introducing optimizers.mp4 10MB
  46. 8. Overfitting/6. Early stopping.mp4 9MB
  47. 3. Setting up the working environment/4. Installing Anaconda.mp4 9MB
  48. 10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4 9MB
  49. 8. Overfitting/3. Training and validation.mp4 9MB
  50. 2. Introduction to neural networks/7. The linear model.mp4 9MB
  51. 6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4 9MB
  52. 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp4 9MB
  53. 2. Introduction to neural networks/3. Training the model.mp4 9MB
  54. 6. Going deeper Introduction to deep neural networks/5. Activation functions.mp4 9MB
  55. 11. Preprocessing/1. Preprocessing introduction.mp4 8MB
  56. 11. Preprocessing/3. Standardization.mp4 8MB
  57. 9. Initialization/1. Initialization - Introduction.mp4 8MB
  58. 15. Conclusion/6. An overview of non-NN approaches.mp4 8MB
  59. 10. Gradient descent and learning rates/7. Adaptive moment estimation.mp4 8MB
  60. 5. TensorFlow - An introduction/2. TensorFlow intro.mp4 8MB
  61. 2. Introduction to neural networks/10. The linear model. Multiple inputs.mp4 8MB
  62. 8. Overfitting/4. Training, validation, and test.mp4 7MB
  63. 6. Going deeper Introduction to deep neural networks/6. Softmax activation.vtt 7MB
  64. 6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7MB
  65. 12. The MNIST example/1. The dataset.mp4 7MB
  66. 12. The MNIST example/2. How to tackle the MNIST.mp4 7MB
  67. 2. Introduction to neural networks/18. L2-norm loss.mp4 7MB
  68. 12. The MNIST example/5. Declaring the loss and the optimization algorithm.mp4 7MB
  69. 8. Overfitting/5. N-fold cross validation.mp4 7MB
  70. 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4 7MB
  71. 8. Overfitting/2. Underfitting and overfitting - classification.mp4 7MB
  72. 6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 7MB
  73. 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 7MB
  74. 6. Going deeper Introduction to deep neural networks/7. Backpropagation.vtt 7MB
  75. 2. Introduction to neural networks/14. Graphical representation.mp4 6MB
  76. 15. Conclusion/2. What’s further out there in the machine and deep learning world.mp4 6MB
  77. 11. Preprocessing/5. One-hot and binary encoding.mp4 6MB
  78. 10. Gradient descent and learning rates/3. Momentum.mp4 6MB
  79. 11. Preprocessing/4. Dealing with categorical data.mp4 6MB
  80. 5. TensorFlow - An introduction/3. Types of file formats in TensorFlow.mp4 6MB
  81. 9. Initialization/3. Xavier initialization.mp4 6MB
  82. 2. Introduction to neural networks/16. The objective function.mp4 6MB
  83. 9. Initialization/2. Types of simple initializations.mp4 6MB
  84. 3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 6MB
  85. 12. The MNIST example/3. Importing the relevant packages.mp4 5MB
  86. 13. Business case/9. Interpreting the result.mp4 5MB
  87. 15. Conclusion/5. An overview of RNNs.mp4 5MB
  88. 3. Setting up the working environment/9. Installing the TensorFlow package.mp4 5MB
  89. 6. Going deeper Introduction to deep neural networks/1. Layers.mp4 5MB
  90. 12. The MNIST example/7. Batching and early stopping.mp4 5MB
  91. 10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp4 4MB
  92. 13. Business case/10. Testing the model.mp4 4MB
  93. 13. Business case/2. Outlining the business case solution.mp4 4MB
  94. 11. Preprocessing/2. Basic preprocessing.mp4 4MB
  95. 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp4 3MB
  96. 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 3MB
  97. 6. Going deeper Introduction to deep neural networks/1.1 Course Notes - Section 6.pdf.pdf 936KB
  98. 6. Going deeper Introduction to deep neural networks/2.1 Course Notes - Section 6.pdf.pdf 936KB
  99. 2. Introduction to neural networks/1.1 Course Notes - Section 2.pdf.pdf 928KB
  100. 2. Introduction to neural networks/10.1 Course Notes - Section 2.pdf.pdf 928KB
  101. 2. Introduction to neural networks/12.1 Course Notes - Section 2.pdf.pdf 928KB
  102. 2. Introduction to neural networks/14.1 Course Notes - Section 2.pdf.pdf 928KB
  103. 2. Introduction to neural networks/16.1 Course Notes - Section 2.pdf.pdf 928KB
  104. 2. Introduction to neural networks/18.1 Course Notes - Section 2.pdf.pdf 928KB
  105. 2. Introduction to neural networks/20.1 Course Notes - Section 2.pdf.pdf 928KB
  106. 2. Introduction to neural networks/22.2 Course Notes - Section 2.pdf.pdf 928KB
  107. 2. Introduction to neural networks/24.1 Course Notes - Section 2.pdf.pdf 928KB
  108. 2. Introduction to neural networks/3.1 Course Notes - Section 2.pdf.pdf 928KB
  109. 2. Introduction to neural networks/5.1 Course Notes - Section 2.pdf.pdf 928KB
  110. 2. Introduction to neural networks/7.1 Course Notes - Section 2.pdf.pdf 928KB
  111. 13. Business case/1.1 Audiobooks_data.csv.csv 711KB
  112. 3. Setting up the working environment/7.1 Shortcuts for Jupyter.pdf.pdf 619KB
  113. 7. Backpropagation. A peek into the Mathematics of Optimization/1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf 182KB
  114. 2. Introduction to neural networks/22.1 GD-function-example.xlsx.xlsx 42KB
  115. 13. Business case/4. Preprocessing the data.vtt 12KB
  116. 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.vtt 10KB
  117. 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.vtt 9KB
  118. 13. Business case/1. Exploring the dataset and identifying predictors.vtt 9KB
  119. 12. The MNIST example/8. Learning.vtt 9KB
  120. 1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.vtt 9KB
  121. 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.vtt 8KB
  122. 12. The MNIST example/4. Outlining the model.vtt 8KB
  123. 2. Introduction to neural networks/22. One parameter gradient descent.vtt 7KB
  124. 12. The MNIST example/9. Discuss the results and test.vtt 7KB
  125. 5. TensorFlow - An introduction/6. Model output.vtt 7KB
  126. 13. Business case/6. Create a class for batching.vtt 7KB
  127. 2. Introduction to neural networks/24. N-parameter gradient descent.vtt 7KB
  128. 5. TensorFlow - An introduction/4. Inputs, outputs, targets, weights, biases - model layout.vtt 6KB
  129. 13. Business case/7. Outlining the model.vtt 6KB
  130. 8. Overfitting/6. Early stopping.vtt 6KB
  131. 3. Setting up the working environment/6. The Jupyter dashboard - part 2.vtt 6KB
  132. 4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.vtt 6KB
  133. 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.vtt 6KB
  134. 13. Business case/8. Optimizing the algorithm.vtt 6KB
  135. 15. Conclusion/3. An overview of CNNs.vtt 6KB
  136. 3. Setting up the working environment/2. Why Python and why Jupyter.vtt 6KB
  137. 1. Welcome! Course introduction/2. What does the course cover.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. 2. Introduction to neural networks/1. Introduction to neural networks.vtt 5KB
  142. 8. Overfitting/1. Underfitting and overfitting.vtt 5KB
  143. 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.vtt 5KB
  144. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.vtt 5KB
  145. 2. Introduction to neural networks/5. Types of machine learning.vtt 5KB
  146. 13. Business case/11. A comment on the homework.vtt 5KB
  147. 2. Introduction to neural networks/20. Cross-entropy loss.vtt 5KB
  148. 15. Conclusion/1. See how much you have learned.vtt 5KB
  149. 5. TensorFlow - An introduction/1. TensorFlow outline.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. 12. The MNIST example/6. Accuracy of prediction.vtt 5KB
  153. 6. Going deeper Introduction to deep neural networks/5. Activation functions.vtt 5KB
  154. 8. Overfitting/3. Training and validation.vtt 4KB
  155. 10. Gradient descent and learning rates/1. Stochastic gradient descent.vtt 4KB
  156. 5. TensorFlow - An introduction/5. Loss function and gradient descent - introducing optimizers.vtt 4KB
  157. 11. Preprocessing/5. One-hot and binary encoding.vtt 4KB
  158. 3. Setting up the working environment/4. Installing Anaconda.vtt 4KB
  159. 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.vtt 4KB
  160. 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.vtt 4KB
  161. 13. Business case/3. Balancing the dataset.vtt 4KB
  162. 2. Introduction to neural networks/3. Training the model.vtt 4KB
  163. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.vtt 4KB
  164. 8. Overfitting/5. N-fold cross validation.vtt 4KB
  165. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.vtt 4KB
  166. 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.vtt 4KB
  167. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.vtt 3KB
  168. 2. Introduction to neural networks/7. The linear model.vtt 3KB
  169. 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.vtt 3KB
  170. 11. Preprocessing/1. Preprocessing introduction.vtt 3KB
  171. 6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.vtt 3KB
  172. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.vtt 3KB
  173. 15. Conclusion/5. An overview of RNNs.vtt 3KB
  174. 9. Initialization/3. Xavier initialization.vtt 3KB
  175. 9. Initialization/2. Types of simple initializations.vtt 3KB
  176. 12. The MNIST example/2. How to tackle the MNIST.vtt 3KB
  177. 14. Appendix Linear Algebra Fundamentals/5. Tensors.vtt 3KB
  178. 12. The MNIST example/5. Declaring the loss and the optimization algorithm.vtt 3KB
  179. 9. Initialization/1. Initialization - Introduction.vtt 3KB
  180. 10. Gradient descent and learning rates/3. Momentum.vtt 3KB
  181. 8. Overfitting/4. Training, validation, and test.vtt 3KB
  182. 12. The MNIST example/1. The dataset.vtt 3KB
  183. 5. TensorFlow - An introduction/3. Types of file formats in TensorFlow.vtt 3KB
  184. 10. Gradient descent and learning rates/7. Adaptive moment estimation.vtt 3KB
  185. 6. Going deeper Introduction to deep neural networks/2. What is a deep net.vtt 3KB
  186. 3. Setting up the working environment/9. Installing the TensorFlow package.vtt 3KB
  187. 3. Setting up the working environment/5. The Jupyter dashboard - part 1.vtt 3KB
  188. 2. Introduction to neural networks/10. The linear model. Multiple inputs.vtt 3KB
  189. 13. Business case/9. Interpreting the result.vtt 3KB
  190. 16. Bonus lecture/1. Bonus lecture Next steps.html 3KB
  191. 10. Gradient descent and learning rates/2. Gradient descent pitfalls.vtt 3KB
  192. 12. The MNIST example/7. Batching and early stopping.vtt 2KB
  193. 2. Introduction to neural networks/18. L2-norm loss.vtt 2KB
  194. 11. Preprocessing/4. Dealing with categorical data.vtt 2KB
  195. 8. Overfitting/2. Underfitting and overfitting - classification.vtt 2KB
  196. 2. Introduction to neural networks/14. Graphical representation.vtt 2KB
  197. 13. Business case/10. Testing the model.vtt 2KB
  198. 12. The MNIST example/10. MNIST - exercises.html 2KB
  199. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.vtt 2KB
  200. 15. Conclusion/2. What’s further out there in the machine and deep learning world.vtt 2KB
  201. 13. Business case/2. Outlining the business case solution.vtt 2KB
  202. 12. The MNIST example/11. MNIST - solutions.html 2KB
  203. 6. Going deeper Introduction to deep neural networks/1. Layers.vtt 2KB
  204. 12. The MNIST example/3. Importing the relevant packages.vtt 2KB
  205. 5. TensorFlow - An introduction/2. TensorFlow intro.vtt 2KB
  206. 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.vtt 2KB
  207. 2. Introduction to neural networks/16. The objective function.vtt 2KB
  208. 5. TensorFlow - An introduction/7. Minimal example - Exercises.html 2KB
  209. 4. Minimal example - your first machine learning algorithm/5. Minimal example - Exercises.html 2KB
  210. 11. Preprocessing/2. Basic preprocessing.vtt 1KB
  211. 15. Conclusion/4. How DeepMind uses deep learning.html 1KB
  212. 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.vtt 1KB
  213. 2. Introduction to neural networks/9. Need Help with Linear Algebra.html 829B
  214. 7. 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 441B
  216. 13. Business case/5. Preprocessing exercise.html 394B
  217. 3. Setting up the working environment/11. Installing packages - solution.html 339B
  218. 3. Setting up the working environment/7. Jupyter Shortcuts.html 332B
  219. 3. 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. 14. Appendix Linear Algebra Fundamentals/4.1 Scalars, Vectors and Matrices Python Notebook.html 181B
  222. 14. Appendix Linear Algebra Fundamentals/6.1 Addition and Subtraction Python Notebook.html 178B
  223. 12. The MNIST example/11.11 TensorFlow_MNIST_Activation_functions_Part_1_Solution.html 172B
  224. 12. The MNIST example/11.9 MNIST_Activation_functions_Part_2_Solution.html 172B
  225. 14. Appendix Linear Algebra Fundamentals/10.1 Dot Product of Matrices Python Notebook.html 171B
  226. 14. Appendix Linear Algebra Fundamentals/8.1 Transpose of a Matrix Python Notebook.html 167B
  227. 12. The MNIST example/11.10 MNIST_Learning_rate_Part_1_Solution.html 165B
  228. 12. The MNIST example/11.4 MNIST_Learning_rate_Part_2_Solution.html 165B
  229. 12. The MNIST example/11.2 MNIST_take_note_of_time_Solution.html 162B
  230. 12. The MNIST example/11.6 MNIST_Batch_size_Part_2_Solution.html 162B
  231. 12. The MNIST example/11.8 MNIST_Batch_size_Part_1_Solution.html 162B
  232. 5. TensorFlow - An introduction/7.2 TensorFlow_Minimal_Example_Exercise_2_3_Solution.html 162B
  233. 5. TensorFlow - An introduction/7.3 TensorFlow_Minimal_Example_Exercise_2_1_Solution.html 162B
  234. 5. TensorFlow - An introduction/7.6 TensorFlow_Minimal_Example_Exercise_2_2_Solution.html 162B
  235. 5. TensorFlow - An introduction/7.8 TensorFlow_Minimal_Example_Exercise_2_4_Solution.html 162B
  236. 1. Welcome! Course introduction/3. What does the course cover - Quiz.html 161B
  237. 2. Introduction to neural networks/11. The linear model. Multiple inputs - Quiz.html 161B
  238. 2. Introduction to neural networks/13. The linear model. Multiple inputs and multiple outputs - Quiz.html 161B
  239. 2. Introduction to neural networks/15. Graphical representation - Quiz.html 161B
  240. 2. Introduction to neural networks/17. The objective function - Quiz.html 161B
  241. 2. Introduction to neural networks/19. L2-norm loss - Quiz.html 161B
  242. 2. Introduction to neural networks/2. Introduction to neural networks - Quiz.html 161B
  243. 2. Introduction to neural networks/21. Cross-entropy loss - Quiz.html 161B
  244. 2. Introduction to neural networks/23. One parameter gradient descent - Quiz.html 161B
  245. 2. Introduction to neural networks/25. N-parameter gradient descent - Quiz.html 161B
  246. 2. Introduction to neural networks/4. Training the model - Quiz.html 161B
  247. 2. Introduction to neural networks/6. Types of machine learning - Quiz.html 161B
  248. 2. Introduction to neural networks/8. The linear model - Quiz.html 161B
  249. 3. Setting up the working environment/3. Why Python and why Jupyter - Quiz.html 161B
  250. 3. Setting up the working environment/8. The Jupyter dashboard - Quiz.html 161B
  251. 12. The MNIST example/11.3 Width_and_Depth_Solution.html 160B
  252. 5. TensorFlow - An introduction/7.1 TensorFlow_Minimal_Example_Exercise_1_Solution.html 160B
  253. 5. TensorFlow - An introduction/7.5 TensorFlow_Minimal_Example_Exercise_3_Solution.html 160B
  254. 5. TensorFlow - An introduction/7.7 TensorFlow_Minimal_Example_Exercise_4_Solution.html 160B
  255. 12. The MNIST example/3.1 TensorFlow_MNIST_with_comments_Part_1.html 159B
  256. 12. The MNIST example/4.1 TensorFlow_MNIST_with_comments_Part_2.html 159B
  257. 12. The MNIST example/5.1 TensorFlow_MNIST_with_comments_Part_3.html 159B
  258. 12. The MNIST example/6.1 TensorFlow_MNIST_with_comments_Part_4.html 159B
  259. 12. The MNIST example/7.1 TensorFlow_MNIST_with_comments_Part_5.html 159B
  260. 12. The MNIST example/8.1 TensorFlow_MNIST_with_comments_Part_6.html 159B
  261. 12. The MNIST example/11.5 MNIST_around_98_percent_accuracy_solution.html 157B
  262. 5. TensorFlow - An introduction/6.1 TensorFlow - Minimal example complete.html 156B
  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.d. Solution.html 154B
  265. 4. Minimal example - your first machine learning algorithm/5.4 Minimal_example_Exercise_3.b. Solution.html 154B
  266. 4. Minimal example - your first machine learning algorithm/5.7 Minimal_example_Exercise_3.c. Solution.html 154B
  267. 4. Minimal example - your first machine learning algorithm/5.9 Minimal_example_Exercise_3.a. Solution.html 154B
  268. 5. TensorFlow - An introduction/3.1 TensorFlow Minimal example - Part 1.html 154B
  269. 5. TensorFlow - An introduction/4.1 TensorFlow Minimal example - Part 2.html 154B
  270. 5. TensorFlow - An introduction/5.1 TensorFlow Minimal example - Part 3.html 154B
  271. 5. TensorFlow - An introduction/7.4 TensorFlow_Minimal_Example_All_Exercises.html 154B
  272. 12. The MNIST example/9.1 TensorFlow_MNIST_with_comments.html 152B
  273. 12. The MNIST example/11.1 MNIST_Depth_Solution.html 150B
  274. 12. The MNIST example/11.7 MNIST_Width_Solution.html 150B
  275. 4. Minimal example - your first machine learning algorithm/5.1 Minimal_example_Exercise_2_Solution.html 149B
  276. 4. Minimal example - your first machine learning algorithm/5.10 Minimal_example_Exercise_6_Solution.html 149B
  277. 4. Minimal example - your first machine learning algorithm/5.3 Minimal_example_Exercise_4_Solution.html 149B
  278. 4. Minimal example - your first machine learning algorithm/5.6 Minimal_example_Exercise_1_Solution.html 149B
  279. 4. Minimal example - your first machine learning algorithm/5.8 Minimal_example_Exercise_5_Solution.html 149B
  280. 14. Appendix Linear Algebra Fundamentals/5.1 Tensors Notebook.html 148B
  281. 4. Minimal example - your first machine learning algorithm/4.1 Minimal example - part 4.html 145B
  282. 12. The MNIST example/10.1 MNIST_Exercises_All.html 144B
  283. 4. Minimal example - your first machine learning algorithm/5.5 Minimal_example_All_Exercises.html 143B
  284. 4. Minimal example - your first machine learning algorithm/1.1 Minimal example Part 1.html 136B
  285. 4. Minimal example - your first machine learning algorithm/2.1 Minimal example - part 2.html 136B
  286. 4. Minimal example - your first machine learning algorithm/3.1 Minimal example - part 3.html 136B
  287. 13. Business case/11.1 Homework exercise.html 134B
  288. 13. Business case/12.1 Homework exercise.html 134B
  289. 13. Business case/4.1 Preprocessing.html 134B
  290. 13. Business case/5.1 Preprocessing exercise.html 134B
  291. 13. Business case/6.1 Class.html 134B
  292. 13. Business case/7.1 Outlining the model.html 134B
  293. 13. Business case/8.1 Optimizing the algorithm.html 134B
  294. 13. Business case/9.1 Interpreting the result.html 134B
  295. [DesireCourse.Net].url 51B
  296. [CourseClub.Me].url 48B