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[] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence

  • 收录时间:2020-03-25 13:05:47
  • 文件大小:7GB
  • 下载次数:15
  • 最近下载:2021-01-16 11:27:57
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文件列表

  1. 18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 194MB
  2. 18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 167MB
  3. 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 143MB
  4. 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 124MB
  5. 18. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 123MB
  6. 18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 117MB
  7. 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 103MB
  8. 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 98MB
  9. 4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 92MB
  10. 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 92MB
  11. 5. Convolutional Neural Networks/5. CNN Architecture.mp4 91MB
  12. 2. Google Colab/3. Uploading your own data to Google Colab.mp4 89MB
  13. 18. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 88MB
  14. 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 88MB
  15. 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 87MB
  16. 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 87MB
  17. 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 86MB
  18. 5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 86MB
  19. 4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 84MB
  20. 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 84MB
  21. 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 83MB
  22. 18. Appendix FAQ/5. How to Code Yourself (part 1).mp4 82MB
  23. 4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 81MB
  24. 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 80MB
  25. 10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 78MB
  26. 18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  27. 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 78MB
  28. 5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 77MB
  29. 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 77MB
  30. 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 76MB
  31. 3. Machine Learning and Neurons/1. What is Machine Learning.mp4 73MB
  32. 3. Machine Learning and Neurons/5. Regression Notebook.mp4 72MB
  33. 14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 71MB
  34. 14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 70MB
  35. 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 69MB
  36. 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 68MB
  37. 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 67MB
  38. 3. Machine Learning and Neurons/3. Classification Notebook.mp4 66MB
  39. 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 65MB
  40. 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 64MB
  41. 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 63MB
  42. 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 62MB
  43. 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 61MB
  44. 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 61MB
  45. 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 59MB
  46. 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 59MB
  47. 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 58MB
  48. 7. Natural Language Processing (NLP)/1. Embeddings.mp4 58MB
  49. 18. Appendix FAQ/6. How to Code Yourself (part 2).mp4 56MB
  50. 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 56MB
  51. 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 56MB
  52. 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 56MB
  53. 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 55MB
  54. 3. Machine Learning and Neurons/7. How does a model learn.mp4 55MB
  55. 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 55MB
  56. 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 54MB
  57. 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 53MB
  58. 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 52MB
  59. 2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 51MB
  60. 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 51MB
  61. 14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 50MB
  62. 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 50MB
  63. 3. Machine Learning and Neurons/6. The Neuron.mp4 49MB
  64. 4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 49MB
  65. 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 49MB
  66. 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 49MB
  67. 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 47MB
  68. 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 47MB
  69. 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 47MB
  70. 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 46MB
  71. 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 46MB
  72. 18. Appendix FAQ/9. Is Theano Dead.mp4 44MB
  73. 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 44MB
  74. 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 43MB
  75. 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 43MB
  76. 16. In-Depth Gradient Descent/5. Adam.mp4 43MB
  77. 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 43MB
  78. 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 42MB
  79. 3. Machine Learning and Neurons/8. Making Predictions.mp4 42MB
  80. 7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 41MB
  81. 16. In-Depth Gradient Descent/3. Momentum.mp4 39MB
  82. 5. Convolutional Neural Networks/9. Data Augmentation.mp4 39MB
  83. 1. Welcome/1. Introduction.mp4 39MB
  84. 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 39MB
  85. 18. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp4 39MB
  86. 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 39MB
  87. 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 38MB
  88. 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 38MB
  89. 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 38MB
  90. 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 38MB
  91. 15. In-Depth Loss Functions/1. Mean Squared Error.mp4 37MB
  92. 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 37MB
  93. 7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 36MB
  94. 16. In-Depth Gradient Descent/1. Gradient Descent.mp4 36MB
  95. 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 35MB
  96. 3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 35MB
  97. 5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 35MB
  98. 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 33MB
  99. 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 32MB
  100. 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 32MB
  101. 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 31MB
  102. 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 31MB
  103. 1. Welcome/2. Outline.mp4 31MB
  104. 1. Welcome/3. Where to get the code.mp4 30MB
  105. 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 30MB
  106. 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 30MB
  107. 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 30MB
  108. 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 29MB
  109. 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 27MB
  110. 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 25MB
  111. 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 25MB
  112. 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 24MB
  113. 5. Convolutional Neural Networks/10. Batch Normalization.mp4 23MB
  114. 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 22MB
  115. 11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 21MB
  116. 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 21MB
  117. 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 20MB
  118. 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 18MB
  119. 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 18MB
  120. 18. Appendix FAQ/1. What is the Appendix.mp4 18MB
  121. 18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.mp4 13MB
  122. 18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28KB
  123. 5. Convolutional Neural Networks/5. CNN Architecture.vtt 24KB
  124. 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.vtt 23KB
  125. 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.vtt 22KB
  126. 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.vtt 21KB
  127. 18. Appendix FAQ/11. What order should I take your courses in (part 2).vtt 20KB
  128. 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.vtt 20KB
  129. 4. Feedforward Artificial Neural Networks/4. Activation Functions.vtt 20KB
  130. 18. Appendix FAQ/5. How to Code Yourself (part 1).vtt 19KB
  131. 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).vtt 18KB
  132. 10. GANs (Generative Adversarial Networks)/1. GAN Theory.vtt 18KB
  133. 5. Convolutional Neural Networks/4. Convolution on Color Images.vtt 18KB
  134. 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.vtt 18KB
  135. 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).vtt 18KB
  136. 5. Convolutional Neural Networks/1. What is Convolution (part 1).vtt 18KB
  137. 18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.vtt 17KB
  138. 5. Convolutional Neural Networks/6. CNN Code Preparation.vtt 17KB
  139. 3. Machine Learning and Neurons/1. What is Machine Learning.vtt 16KB
  140. 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.vtt 16KB
  141. 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.vtt 15KB
  142. 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).vtt 15KB
  143. 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).vtt 14KB
  144. 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).vtt 14KB
  145. 18. Appendix FAQ/10. What order should I take your courses in (part 1).vtt 14KB
  146. 7. Natural Language Processing (NLP)/1. Embeddings.vtt 14KB
  147. 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.vtt 14KB
  148. 4. Feedforward Artificial Neural Networks/6. How to Represent Images.vtt 14KB
  149. 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).vtt 14KB
  150. 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.vtt 13KB
  151. 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).vtt 13KB
  152. 10. GANs (Generative Adversarial Networks)/2. GAN Code.vtt 13KB
  153. 18. Appendix FAQ/8. How to Succeed in this Course (Long Version).vtt 13KB
  154. 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).vtt 13KB
  155. 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).vtt 13KB
  156. 18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 13KB
  157. 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.vtt 12KB
  158. 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.vtt 12KB
  159. 18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.vtt 12KB
  160. 3. Machine Learning and Neurons/7. How does a model learn.vtt 12KB
  161. 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).vtt 12KB
  162. 16. In-Depth Gradient Descent/5. Adam.vtt 12KB
  163. 14. Low-Level Tensorflow/3. Variables and Gradient Tape.vtt 12KB
  164. 14. Low-Level Tensorflow/4. Build Your Own Custom Model.vtt 12KB
  165. 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).vtt 12KB
  166. 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.vtt 12KB
  167. 18. Appendix FAQ/6. How to Code Yourself (part 2).vtt 11KB
  168. 4. Feedforward Artificial Neural Networks/9. ANN for Regression.vtt 11KB
  169. 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).vtt 11KB
  170. 18. Appendix FAQ/9. Is Theano Dead.vtt 11KB
  171. 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.vtt 11KB
  172. 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).vtt 11KB
  173. 3. Machine Learning and Neurons/6. The Neuron.vtt 11KB
  174. 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.vtt 11KB
  175. 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.vtt 11KB
  176. 4. Feedforward Artificial Neural Networks/2. Forward Propagation.vtt 11KB
  177. 3. Machine Learning and Neurons/5. Regression Notebook.vtt 11KB
  178. 2. Google Colab/3. Uploading your own data to Google Colab.vtt 10KB
  179. 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.vtt 10KB
  180. 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.vtt 10KB
  181. 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.vtt 10KB
  182. 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 10KB
  183. 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.vtt 10KB
  184. 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.vtt 10KB
  185. 15. In-Depth Loss Functions/1. Mean Squared Error.vtt 10KB
  186. 5. Convolutional Neural Networks/9. Data Augmentation.vtt 10KB
  187. 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).vtt 10KB
  188. 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.vtt 10KB
  189. 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.vtt 10KB
  190. 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.vtt 9KB
  191. 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).vtt 9KB
  192. 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.vtt 9KB
  193. 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.vtt 9KB
  194. 16. In-Depth Gradient Descent/1. Gradient Descent.vtt 9KB
  195. 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.vtt 9KB
  196. 15. In-Depth Loss Functions/3. Categorical Cross Entropy.vtt 8KB
  197. 7. Natural Language Processing (NLP)/5. CNNs for Text.vtt 8KB
  198. 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.vtt 8KB
  199. 2. Google Colab/2. Tensorflow 2.0 in Google Colab.vtt 8KB
  200. 14. Low-Level Tensorflow/2. Constants and Basic Computation.vtt 8KB
  201. 3. Machine Learning and Neurons/3. Classification Notebook.vtt 8KB
  202. 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).vtt 8KB
  203. 17. Extras/1. Links to TF2.0 Notebooks.html 8KB
  204. 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.vtt 8KB
  205. 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.vtt 8KB
  206. 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.vtt 8KB
  207. 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.vtt 7KB
  208. 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.vtt 7KB
  209. 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.vtt 7KB
  210. 5. Convolutional Neural Networks/3. What is Convolution (part 3).vtt 7KB
  211. 3. Machine Learning and Neurons/8. Making Predictions.vtt 7KB
  212. 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.vtt 7KB
  213. 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.vtt 7KB
  214. 16. In-Depth Gradient Descent/3. Momentum.vtt 7KB
  215. 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.vtt 7KB
  216. 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).vtt 7KB
  217. 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.vtt 7KB
  218. 1. Welcome/3. Where to get the code.vtt 7KB
  219. 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.vtt 7KB
  220. 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).vtt 6KB
  221. 1. Welcome/2. Outline.vtt 6KB
  222. 15. In-Depth Loss Functions/2. Binary Cross Entropy.vtt 6KB
  223. 5. Convolutional Neural Networks/2. What is Convolution (part 2).vtt 6KB
  224. 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.vtt 6KB
  225. 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.vtt 6KB
  226. 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.vtt 6KB
  227. 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.vtt 6KB
  228. 5. Convolutional Neural Networks/10. Batch Normalization.vtt 6KB
  229. 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.vtt 6KB
  230. 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).vtt 6KB
  231. 11. Deep Reinforcement Learning (Theory)/5. The Return.vtt 5KB
  232. 7. Natural Language Processing (NLP)/3. Text Preprocessing.vtt 5KB
  233. 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.vtt 5KB
  234. 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).vtt 5KB
  235. 1. Welcome/1. Introduction.vtt 5KB
  236. 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.vtt 5KB
  237. 5. Convolutional Neural Networks/8. CNN for CIFAR-10.vtt 5KB
  238. 3. Machine Learning and Neurons/9. Saving and Loading a Model.vtt 4KB
  239. 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.vtt 4KB
  240. 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.vtt 4KB
  241. 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).vtt 4KB
  242. 18. Appendix FAQ/1. What is the Appendix.vtt 3KB
  243. 18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.vtt 3KB
  244. 13. Advanced Tensorflow Usage/6. Using the TPU.html 2KB
  245. [DesireCourse.Net].url 51B
  246. [CourseClub.Me].url 48B