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[] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence

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  • 文件大小:7GB
  • 下载次数:15
  • 最近下载:2021-01-19 12:51:28
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文件列表

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