589689.xyz

[] Udemy - A Complete Guide on TensorFlow 2.0 using Keras API

  • 收录时间:2024-05-23 07:17:08
  • 文件大小:5GB
  • 下载次数:1
  • 最近下载:2024-05-23 07:17:08
  • 磁力链接:
立即下载 复制链接

文件列表

  1. 16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4.!qB 194MB
  2. 17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4.!qB 187MB
  3. 1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4.!qB 146MB
  4. 16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.mp4.!qB 137MB
  5. 17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4.!qB 136MB
  6. 7. Deep Reinforcement Learning Theory/9. Action Selection Policies.mp4.!qB 135MB
  7. 17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.mp4.!qB 121MB
  8. 16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4.!qB 118MB
  9. 2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4.!qB 115MB
  10. 7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4.!qB 112MB
  11. 15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4.!qB 112MB
  12. 17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4.!qB 111MB
  13. 16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4.!qB 108MB
  14. 7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp4.!qB 100MB
  15. 15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp4.!qB 99MB
  16. 16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp4.!qB 98MB
  17. 7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp4.!qB 97MB
  18. 7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp4.!qB 95MB
  19. 7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp4.!qB 94MB
  20. 4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.mp4.!qB 88MB
  21. 15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.mp4.!qB 82MB
  22. 7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.mp4.!qB 79MB
  23. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.mp4.!qB 74MB
  24. 2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.mp4.!qB 71MB
  25. 7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.mp4.!qB 69MB
  26. 15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.mp4.!qB 67MB
  27. 3. Artificial Neural Networks/2. Data Preprocessing.mp4.!qB 62MB
  28. 15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.mp4.!qB 61MB
  29. 3. Artificial Neural Networks/3. Building the Artificial Neural Network.mp4.!qB 60MB
  30. 3. Artificial Neural Networks/1. Project Setup.mp4.!qB 59MB
  31. 4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.mp4.!qB 58MB
  32. 16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.mp4.!qB 53MB
  33. 11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.mp4.!qB 53MB
  34. 8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.mp4.!qB 53MB
  35. 6. Transfer Learning and Fine Tuning/2. Project Setup.mp4.!qB 49MB
  36. 2. TensorFlow 2.0 Basics/3. Operations with Tensors.mp4.!qB 49MB
  37. 5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.mp4.!qB 49MB
  38. 3. Artificial Neural Networks/4. Training the Artificial Neural Network.mp4.!qB 49MB
  39. 4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.mp4.!qB 47MB
  40. 6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.mp4.!qB 46MB
  41. 5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.mp4.!qB 46MB
  42. 15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.mp4.!qB 45MB
  43. 15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.mp4.!qB 43MB
  44. 7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.mp4.!qB 43MB
  45. 2. TensorFlow 2.0 Basics/4. Strings.mp4.!qB 40MB
  46. 5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.mp4.!qB 40MB
  47. 11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.mp4.!qB 37MB
  48. 11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.mp4.!qB 35MB
  49. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.mp4 35MB
  50. 8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.mp4.!qB 34MB
  51. 8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.mp4.!qB 33MB
  52. 6. Transfer Learning and Fine Tuning/9. Image Data Generators.mp4.!qB 33MB
  53. 6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.mp4.!qB 32MB
  54. 3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.mp4.!qB 31MB
  55. 8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.mp4.!qB 31MB
  56. 16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.mp4.!qB 30MB
  57. 13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.mp4.!qB 29MB
  58. 14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.mp4.!qB 28MB
  59. 12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.mp4.!qB 28MB
  60. 11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.mp4.!qB 28MB
  61. 12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.mp4.!qB 27MB
  62. 8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.mp4.!qB 27MB
  63. 14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.mp4.!qB 26MB
  64. 12. Image Classification API with TensorFlow Serving/3. Project setup.mp4.!qB 26MB
  65. 12. Image Classification API with TensorFlow Serving/6. Saving the model for production.mp4.!qB 25MB
  66. 8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.mp4.!qB 25MB
  67. 6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.mp4.!qB 25MB
  68. 12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.mp4.!qB 24MB
  69. 9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.mp4.!qB 24MB
  70. 9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.mp4.!qB 24MB
  71. 12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.mp4.!qB 24MB
  72. 12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.mp4.!qB 24MB
  73. 12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.mp4.!qB 23MB
  74. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.mp4 21MB
  75. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.mp4 21MB
  76. 9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.mp4.!qB 21MB
  77. 11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.mp4.!qB 20MB
  78. 9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.mp4.!qB 20MB
  79. 17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.mp4.!qB 20MB
  80. 6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.mp4.!qB 20MB
  81. 12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.mp4.!qB 20MB
  82. 9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.mp4.!qB 19MB
  83. 6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.mp4.!qB 18MB
  84. 6. Transfer Learning and Fine Tuning/10. Transfer Learning.mp4.!qB 17MB
  85. 8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.mp4.!qB 16MB
  86. 16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.mp4.!qB 16MB
  87. 8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.mp4.!qB 16MB
  88. 13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.mp4.!qB 15MB
  89. 13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.mp4 15MB
  90. 14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).mp4.!qB 14MB
  91. 13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.mp4.!qB 14MB
  92. 6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.mp4.!qB 13MB
  93. 6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.mp4.!qB 13MB
  94. 14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.mp4.!qB 12MB
  95. 11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.mp4.!qB 12MB
  96. 11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.mp4.!qB 12MB
  97. 8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.mp4.!qB 12MB
  98. 8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.mp4.!qB 12MB
  99. 15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.mp4.!qB 12MB
  100. 14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.mp4.!qB 11MB
  101. 17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.mp4.!qB 10MB
  102. 6. Transfer Learning and Fine Tuning/14. Fine Tuning.mp4.!qB 10MB
  103. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.mp4 10MB
  104. 12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.mp4.!qB 10MB
  105. 8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.mp4.!qB 10MB
  106. 8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.mp4.!qB 9MB
  107. 13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.mp4.!qB 9MB
  108. 6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.mp4.!qB 9MB
  109. 14. Distributed Training with TensorFlow 2.0/2. Project Setup.mp4.!qB 9MB
  110. 6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.mp4.!qB 9MB
  111. 13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.mp4.!qB 9MB
  112. 13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.mp4.!qB 8MB
  113. 16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.mp4.!qB 8MB
  114. 14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.mp4.!qB 7MB
  115. 6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.mp4.!qB 6MB
  116. 13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.mp4.!qB 6MB
  117. 6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.mp4.!qB 6MB
  118. 9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.mp4.!qB 6MB
  119. 13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.mp4.!qB 5MB
  120. 9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.mp4.!qB 2MB
  121. 11. Fashion API with Flask and TensorFlow 2.0/1.1 Flask API.zip 372KB
  122. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1.2 pollution_small.csv 73KB
  123. 7. Deep Reinforcement Learning Theory/2. The Bellman Equation.srt 31KB
  124. 7. Deep Reinforcement Learning Theory/5. Temporal Difference.srt 29KB
  125. 16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.srt 29KB
  126. 17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.srt 28KB
  127. 7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).srt 27KB
  128. 1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..srt 26KB
  129. 16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.srt 26KB
  130. 15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.srt 25KB
  131. 17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.srt 24KB
  132. 16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.srt 23KB
  133. 16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.srt 22KB
  134. 7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.srt 22KB
  135. 17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.srt 21KB
  136. 16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.srt.!qB 21KB
  137. 17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.srt 21KB
  138. 4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.srt 20KB
  139. 15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.srt 19KB
  140. 15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.srt 19KB
  141. 7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.srt 18KB
  142. 2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.srt 17KB
  143. 3. Artificial Neural Networks/3. Building the Artificial Neural Network.srt 15KB
  144. 15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.srt 14KB
  145. 2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.srt 13KB
  146. 15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.srt 12KB
  147. 15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.srt 12KB
  148. 4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.srt 11KB
  149. 4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.srt 11KB
  150. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.srt 11KB
  151. 3. Artificial Neural Networks/2. Data Preprocessing.srt 11KB
  152. 3. Artificial Neural Networks/4. Training the Artificial Neural Network.srt 10KB
  153. 5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.srt 10KB
  154. 5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.srt 10KB
  155. 7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.srt 10KB
  156. 16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.srt 10KB
  157. 5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.srt 9KB
  158. 2. TensorFlow 2.0 Basics/4. Strings.srt 9KB
  159. 3. Artificial Neural Networks/1. Project Setup.srt 9KB
  160. 2. TensorFlow 2.0 Basics/3. Operations with Tensors.srt 8KB
  161. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.srt 8KB
  162. 11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.srt 8KB
  163. 12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.srt 8KB
  164. 15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.srt 7KB
  165. 12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.srt 7KB
  166. 3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.srt 7KB
  167. 9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.srt 6KB
  168. 6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.srt 6KB
  169. 6. Transfer Learning and Fine Tuning/9. Image Data Generators.srt 6KB
  170. 8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.srt 6KB
  171. 6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.srt 6KB
  172. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.srt 6KB
  173. 16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.srt 6KB
  174. 11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.srt 6KB
  175. 14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.srt 5KB
  176. 16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.srt 5KB
  177. 12. Image Classification API with TensorFlow Serving/6. Saving the model for production.srt 5KB
  178. 13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.srt 5KB
  179. 12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.srt 5KB
  180. 12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.srt 5KB
  181. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.srt 5KB
  182. 12. Image Classification API with TensorFlow Serving/3. Project setup.srt 5KB
  183. 17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.srt 5KB
  184. 6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.srt 5KB
  185. 6. Transfer Learning and Fine Tuning/2. Project Setup.srt 5KB
  186. 12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.srt 4KB
  187. 11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.srt 4KB
  188. 14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.srt 4KB
  189. 14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.srt 4KB
  190. 13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.srt 4KB
  191. 6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.srt 4KB
  192. 15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.srt 4KB
  193. 11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.srt 4KB
  194. 6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.srt 4KB
  195. 14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).srt 4KB
  196. 17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.srt 4KB
  197. 6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.srt 4KB
  198. 12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.srt 3KB
  199. 8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.srt 3KB
  200. 12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.srt 3KB
  201. 13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.srt 3KB
  202. 8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.srt 3KB
  203. 6. Transfer Learning and Fine Tuning/10. Transfer Learning.srt 3KB
  204. 8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.srt 3KB
  205. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.srt 3KB
  206. 11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.srt 3KB
  207. 16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.srt 3KB
  208. 6. Transfer Learning and Fine Tuning/14. Fine Tuning.srt 3KB
  209. 13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.srt 3KB
  210. 11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.srt 3KB
  211. 13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.srt 2KB
  212. 12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.srt 2KB
  213. 6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.srt 2KB
  214. 14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.srt 2KB
  215. 18. Bonus Lectures/3. FREE LEARNING RESOURCES FOR YOU.html 2KB
  216. 14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.srt 2KB
  217. 13. TensorFlow Lite Prepare a model for a mobile device/10. What's next.html 2KB
  218. 13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.srt 2KB
  219. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/6. What's next.html 2KB
  220. 14. Distributed Training with TensorFlow 2.0/2. Project Setup.srt 2KB
  221. 13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.srt 2KB
  222. 6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.srt 2KB
  223. 1. Introduction/4. BONUS Learning Path.html 2KB
  224. 13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.srt 2KB
  225. 11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.srt 2KB
  226. 6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.srt 2KB
  227. 6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.srt 2KB
  228. 6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.srt 1KB
  229. 13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.srt 1KB
  230. 18. Bonus Lectures/2. YOUR SPECIAL BONUS.html 1KB
  231. 9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.srt 840B
  232. 18. Bonus Lectures/1. SPECIAL COVID-19 BONUS.html 722B
  233. 1. Introduction/3. BONUS 10 advantages of TensorFlow.html 613B
  234. 4. Convolutional Neural Networks/6. HOMEWORK SOLUTION Convolutional Neural Networks.html 573B
  235. 4. Convolutional Neural Networks/5. HOMEWORK Convolutional Neural Networks.html 500B
  236. 3. Artificial Neural Networks/7. HOMEWORK Artificial Neural Networks.html 493B
  237. 1. Introduction/2. Course Curriculum & Colab Toolkit.html 464B
  238. 3. Artificial Neural Networks/8. HOMEWORK SOLUTION Artificial Neural Networks.html 421B
  239. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1.1 Google Colab TFT.html 134B
  240. 12. Image Classification API with TensorFlow Serving/3.1 Google Colab TensorFlow Serving.html 134B
  241. 13. TensorFlow Lite Prepare a model for a mobile device/2.1 Google Colab TensorFlow Lite.html 134B
  242. 14. Distributed Training with TensorFlow 2.0/2.1 Google Colab Distributed Training.html 134B
  243. 2. TensorFlow 2.0 Basics/1.1 Google Colab TensorFlow 1.x to TensorFlow 2.0.html 134B
  244. 3. Artificial Neural Networks/1.1 Google Colab ANN.html 134B
  245. 4. Convolutional Neural Networks/1.1 Google Colab CNN.html 134B
  246. 5. Recurrent Neural Networks/1.1 Google Colab RNN.html 134B
  247. 6. Transfer Learning and Fine Tuning/2.1 Google Colab Transfer Learning and Fine Tuning.html 134B
  248. 8. Deep Reinforcement Learning for Stock Market trading/1.1 Google Colab Deep-Q Trading Bot.html 134B
  249. 3. Artificial Neural Networks/6. Artificial Neural Network Quiz.html 123B
  250. 4. Convolutional Neural Networks/4. Convolutional Neural Networks Quiz.html 123B
  251. 5. Recurrent Neural Networks/4. Recurrent Neural Network Quiz.html 123B
  252. 6. Transfer Learning and Fine Tuning/16. Transfer Learning quiz.html 123B
  253. 0. Websites you may like/[DesireCourse.Net].url 51B
  254. 1. Introduction/[DesireCourse.Net].url 51B
  255. 14. Distributed Training with TensorFlow 2.0/[DesireCourse.Net].url 51B
  256. 0. Websites you may like/[CourseClub.Me].url 48B
  257. 1. Introduction/[CourseClub.Me].url 48B
  258. 14. Distributed Training with TensorFlow 2.0/[CourseClub.Me].url 48B