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PyTorch Deep Learning and Artificial Intelligence updated

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PyTorch Deep Learning and Artificial Intelligence updated
Language: English
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Size: 7.3 GB
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Files:
  1. 2. Windows-Focused Environment Setup 2018.mp4 180.7 MB
  2. READ_ME.txt 404 bytes
  3. 1. Welcome.mp4 35.7 MB
  4. 1. Welcome.srt 5.7 KB
  5. 2. Overview and Outline.mp4 79.7 MB
  6. 2. Overview and Outline.srt 17.7 KB
  7. 3. Where to get the Code.mp4 30.2 MB
  8. 3. Where to get the Code.srt 7.6 KB
  9. 1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 60.5 MB
  10. 1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14.3 KB
  11. 2. Uploading your own data to Google Colab.mp4 90.5 MB
  12. 2. Uploading your own data to Google Colab.srt 14.5 KB
  13. 3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 44.4 MB
  14. 3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 12.1 KB
  15. 1. What is Machine Learning.mp4 70.6 MB
  16. 1. What is Machine Learning.srt 18.4 KB
  17. 2. Regression Basics.mp4 73.0 MB
  18. 2. Regression Basics.srt 20.1 KB
  19. 3. Regression Code Preparation.mp4 45.5 MB
  20. 3. Regression Code Preparation.srt 16.4 KB
  21. 4. Regression Notebook.mp4 71.9 MB
  22. 4. Regression Notebook.srt 17.5 KB
  23. 5. Moore's Law.mp4 30.6 MB
  24. 5. Moore's Law.srt 9.1 KB
  25. 6. Moore's Law Notebook.mp4 78.9 MB
  26. 6. Moore's Law Notebook.srt 15.8 KB
  27. 7. Linear Classification Basics.mp4 67.2 MB
  28. 7. Linear Classification Basics.srt 19.8 KB
  29. 8. Classification Code Preparation.mp4 26.5 MB
  30. 8. Classification Code Preparation.srt 9.4 KB
  31. 9. Classification Notebook.mp4 78.3 MB
  32. 9. Classification Notebook.srt 14.6 KB
  33. 10. Saving and Loading a Model.mp4 28.8 MB
  34. 10. Saving and Loading a Model.srt 6.6 KB
  35. 11. A Short Neuroscience Primer.mp4 44.7 MB
  36. 11. A Short Neuroscience Primer.srt 12.3 KB
  37. 12. How does a model learn.mp4 50.1 MB
  38. 12. How does a model learn.srt 13.8 KB
  39. 13. Model With Logits.mp4 27.3 MB
  40. 13. Model With Logits.srt 5.3 KB
  41. 14. Train Sets vs. Validation Sets vs. Test Sets.mp4 52.1 MB
  42. 14. Train Sets vs. Validation Sets vs. Test Sets.srt 14.3 KB
  43. 1. Artificial Neural Networks Section Introduction.mp4 33.5 MB
  44. 1. Artificial Neural Networks Section Introduction.srt 7.9 KB
  45. 2. Forward Propagation.mp4 47.1 MB
  46. 2. Forward Propagation.srt 12.2 KB
  47. 3. The Geometrical Picture.mp4 56.4 MB
  48. 3. The Geometrical Picture.srt 11.5 KB
  49. 4. Activation Functions.mp4 89.2 MB
  50. 4. Activation Functions.srt 22.6 KB
  51. 5. Multiclass Classification.mp4 48.7 MB
  52. 5. Multiclass Classification.srt 12.2 KB
  53. 6. How to Represent Images.mp4 75.4 MB
  54. 6. How to Represent Images.srt 15.3 KB
  55. 7. Code Preparation (ANN).mp4 67.5 MB
  56. 7. Code Preparation (ANN).srt 19.9 KB
  57. 8. ANN for Image Classification.mp4 106.3 MB
  58. 8. ANN for Image Classification.srt 22.6 KB
  59. 9. ANN for Regression.mp4 80.2 MB
  60. 9. ANN for Regression.srt 13.0 KB
  61. 1. What is Convolution (part 1).mp4 79.7 MB
  62. 1. What is Convolution (part 1).srt 20.1 KB
  63. 2. What is Convolution (part 2).mp4 24.5 MB
  64. 2. What is Convolution (part 2).srt 7.2 KB
  65. 3. What is Convolution (part 3).mp4 28.7 MB
  66. 3. What is Convolution (part 3).srt 8.0 KB
  67. 4. Convolution on Color Images.mp4 76.4 MB
  68. 4. Convolution on Color Images.srt 20.8 KB
  69. 5. CNN Architecture.mp4 89.5 MB
  70. 5. CNN Architecture.srt 27.8 KB
  71. 6. CNN Code Preparation (part 1).mp4 76.7 MB
  72. 6. CNN Code Preparation (part 1).srt 22.8 KB
  73. 7. CNN Code Preparation (part 2).mp4 36.7 MB
  74. 7. CNN Code Preparation (part 2).srt 10.4 KB
  75. 8. CNN Code Preparation (part 3).mp4 33.7 MB
  76. 8. CNN Code Preparation (part 3).srt 7.2 KB
  77. 9. CNN for Fashion MNIST.mp4 74.5 MB
  78. 9. CNN for Fashion MNIST.srt 13.5 KB
  79. 10. CNN for CIFAR-10.mp4 56.7 MB
  80. 10. CNN for CIFAR-10.srt 9.3 KB
  81. 11. Data Augmentation.mp4 44.5 MB
  82. 11. Data Augmentation.srt 12.5 KB
  83. 12. Batch Normalization.mp4 23.4 MB
  84. 12. Batch Normalization.srt 6.6 KB
  85. 13. Improving CIFAR-10 Results.mp4 77.4 MB
  86. 13. Improving CIFAR-10 Results.srt 12.8 KB
  87. 1. Sequence Data.mp4 114.3 MB
  88. 1. Sequence Data.srt 29.6 KB
  89. 2. Forecasting.mp4 48.7 MB
  90. 2. Forecasting.srt 13.2 KB
  91. 3. Autoregressive Linear Model for Time Series Prediction.mp4 81.2 MB
  92. 3. Autoregressive Linear Model for Time Series Prediction.srt 14.7 KB
  93. 4. Proof that the Linear Model Works.mp4 17.9 MB
  94. 4. Proof that the Linear Model Works.srt 4.6 KB
  95. 5. Recurrent Neural Networks.mp4 92.6 MB
  96. 5. Recurrent Neural Networks.srt 25.7 KB
  97. 6. RNN Code Preparation.mp4 55.3 MB
  98. 6. RNN Code Preparation.srt 17.6 KB
  99. 7. RNN for Time Series Prediction.mp4 71.9 MB
  100. 7. RNN for Time Series Prediction.srt 9.9 KB
  101. 8. Paying Attention to Shapes.mp4 56.4 MB
  102. 8. Paying Attention to Shapes.srt 11.0 KB
  103. 9. GRU and LSTM (pt 1).mp4 76.1 MB
  104. 9. GRU and LSTM (pt 1).srt 21.1 KB
  105. 10. GRU and LSTM (pt 2).mp4 50.6 MB
  106. 10. GRU and LSTM (pt 2).srt 15.0 KB
  107. 11. A More Challenging Sequence.mp4 86.7 MB
  108. 11. A More Challenging Sequence.srt 10.7 KB
  109. 12. RNN for Image Classification (Theory).mp4 32.3 MB
  110. 12. RNN for Image Classification (Theory).srt 6.0 KB
  111. 13. RNN for Image Classification (Code).mp4 20.5 MB
  112. 13. RNN for Image Classification (Code).srt 3.3 KB
  113. 14. Stock Return Predictions using LSTMs (pt 1).mp4 77.8 MB
  114. 14. Stock Return Predictions using LSTMs (pt 1).srt 16.0 KB
  115. 15. Stock Return Predictions using LSTMs (pt 2).mp4 43.2 MB
  116. 15. Stock Return Predictions using LSTMs (pt 2).srt 6.8 KB
  117. 16. Stock Return Predictions using LSTMs (pt 3).mp4 71.1 MB
  118. 16. Stock Return Predictions using LSTMs (pt 3).srt 14.4 KB
  119. 17. Other Ways to Forecast.mp4 28.3 MB
  120. 17. Other Ways to Forecast.srt 7.2 KB
  121. 1. Embeddings.mp4 60.0 MB
  122. 1. Embeddings.srt 16.1 KB
  123. 2. Neural Networks with Embeddings.mp4 15.6 MB
  124. 2. Neural Networks with Embeddings.srt 4.5 KB
  125. 3. Text Preprocessing (pt 1).mp4 52.3 MB
  126. 3. Text Preprocessing (pt 1).srt 17.9 KB
  127. 4. Text Preprocessing (pt 2).mp4 44.4 MB
  128. 4. Text Preprocessing (pt 2).srt 15.3 KB
  129. 5. Text Preprocessing (pt 3).mp4 47.7 MB
  130. 5. Text Preprocessing (pt 3).srt 9.4 KB
  131. 6. Text Classification with LSTMs.mp4 65.0 MB
  132. 6. Text Classification with LSTMs.srt 10.3 KB
  133. 7. CNNs for Text.mp4 58.7 MB
  134. 7. CNNs for Text.srt 14.9 KB
  135. 8. Text Classification with CNNs.mp4 39.3 MB
  136. 8. Text Classification with CNNs.srt 5.6 KB
  137. 9. VIP Making Predictions with a Trained NLP Model.mp4 48.8 MB
  138. 9. VIP Making Predictions with a Trained NLP Model.srt 9.1 KB
  139. 1. Recommender Systems with Deep Learning Theory.mp4 64.8 MB
  140. 1. Recommender Systems with Deep Learning Theory.srt 13.7 KB
  141. 2. Recommender Systems with Deep Learning Code Preparation.mp4 40.1 MB
  142. 2. Recommender Systems with Deep Learning Code Preparation.srt 12.7 KB
  143. 3. Recommender Systems with Deep Learning Code (pt 1).mp4 69.6 MB
  144. 3. Recommender Systems with Deep Learning Code (pt 1).srt 10.9 KB
  145. 4. Recommender Systems with Deep Learning Code (pt 2).mp4 76.9 MB
  146. 4. Recommender Systems with Deep Learning Code (pt 2).srt 17.4 KB
  147. 5. VIP Making Predictions with a Trained Recommender Model.mp4 32.7 MB
  148. 5. VIP Making Predictions with a Trained Recommender Model.srt 6.0 KB
  149. 1. Transfer Learning Theory.mp4 58.2 MB
  150. 1. Transfer Learning Theory.srt 10.7 KB
  151. 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 21.7 MB
  152. 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt 5.2 KB
  153. 3. Large Datasets.mp4 41.3 MB
  154. 3. Large Datasets.srt 9.1 KB
  155. 4. 2 Approaches to Transfer Learning.mp4 21.8 MB
  156. 4. 2 Approaches to Transfer Learning.srt 6.0 KB
  157. 5. Transfer Learning Code (pt 1).mp4 77.8 MB
  158. 5. Transfer Learning Code (pt 1).srt 11.6 KB
  159. 6. Transfer Learning Code (pt 2).mp4 56.3 MB
  160. 6. Transfer Learning Code (pt 2).srt 8.8 KB
  161. 1. GAN Theory.mp4 92.1 MB
  162. 1. GAN Theory.srt 21.1 KB
  163. 2. GAN Code Preparation.mp4 28.1 MB
  164. 2. GAN Code Preparation.srt 8.5 KB
  165. 3. GAN Code.mp4 61.4 MB
  166. 3. GAN Code.srt 10.7 KB
  167. 1. Deep Reinforcement Learning Section Introduction.mp4 40.7 MB
  168. 1. Deep Reinforcement Learning Section Introduction.srt 8.6 KB
  169. 2. Elements of a Reinforcement Learning Problem.mp4 104.9 MB
  170. 2. Elements of a Reinforcement Learning Problem.srt 26.2 KB
  171. 3. States, Actions, Rewards, Policies.mp4 44.1 MB
  172. 3. States, Actions, Rewards, Policies.srt 11.3 KB
  173. 4. Markov Decision Processes (MDPs).mp4 50.5 MB
  174. 4. Markov Decision Processes (MDPs).srt 12.7 KB
  175. 5. The Return.mp4 23.4 MB
  176. 5. The Return.srt 6.3 KB
  177. 6. Value Functions and the Bellman Equation.mp4 47.7 MB
  178. 6. Value Functions and the Bellman Equation.srt 12.5 KB
  179. 7. What does it mean to “learn”.mp4 32.5 MB
  180. 7. What does it mean to “learn”.srt 8.9 KB
  181. 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 42.6 MB
  182. 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12.7 KB
  183. 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 57.0 MB
  184. 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 14.9 KB
  185. 10. Epsilon-Greedy.mp4 41.5 MB
  186. 10. Epsilon-Greedy.srt 7.4 KB
  187. 11. Q-Learning.mp4 66.8 MB
  188. 11. Q-Learning.srt 17.9 KB
  189. 12. Deep Q-Learning DQN (pt 1).mp4 60.2 MB
  190. 12. Deep Q-Learning DQN (pt 1).srt 16.4 KB
  191. 13. Deep Q-Learning DQN (pt 2).mp4 52.2 MB
  192. 13. Deep Q-Learning DQN (pt 2).srt 13.2 KB
  193. 14. How to Learn Reinforcement Learning.mp4 40.3 MB
  194. 14. How to Learn Reinforcement Learning.srt 7.6 KB
  195. 1. Reinforcement Learning Stock Trader Introduction.mp4 28.8 MB
  196. 1. Reinforcement Learning Stock Trader Introduction.srt 6.8 KB
  197. 2. Data and Environment.mp4 55.7 MB
  198. 2. Data and Environment.srt 15.7 KB
  199. 3. Replay Buffer.mp4 25.0 MB
  200. 3. Replay Buffer.srt 6.9 KB
  201. 4. Program Design and Layout.mp4 26.9 MB
  202. 4. Program Design and Layout.srt 8.6 KB
  203. 5. Code pt 1.mp4 66.3 MB
  204. 5. Code pt 1.srt 12.1 KB
  205. 6. Code pt 2.mp4 70.0 MB
  206. 6. Code pt 2.srt 11.8 KB
  207. 7. Code pt 3.mp4 58.6 MB
  208. 7. Code pt 3.srt 8.4 KB
  209. 8. Code pt 4.mp4 52.3 MB
  210. 8. Code pt 4.srt 8.2 KB
  211. 9. Reinforcement Learning Stock Trader Discussion.mp4 17.2 MB
  212. 9. Reinforcement Learning Stock Trader Discussion.srt 4.4 KB
  213. 1. Custom Loss and Estimating Prediction Uncertainty.mp4 43.6 MB
  214. 1. Custom Loss and Estimating Prediction Uncertainty.srt 12.8 KB
  215. 2. Estimating Prediction Uncertainty Code.mp4 42.7 MB
  216. 2. Estimating Prediction Uncertainty Code.srt 8.8 KB
  217. 1. Facial Recognition Section Introduction.mp4 24.3 MB
  218. 1. Facial Recognition Section Introduction.srt 4.6 KB
  219. 2. Siamese Networks.mp4 50.5 MB
  220. 2. Siamese Networks.srt 12.8 KB
  221. 3. Code Outline.mp4 23.9 MB
  222. 3. Code Outline.srt 5.8 KB
  223. 4. Loading in the data.mp4 35.1 MB
  224. 4. Loading in the data.srt 6.9 KB
  225. 5. Splitting the data into train and test.mp4 26.3 MB
  226. 5. Splitting the data into train and test.srt 5.1 KB
  227. 6. Converting the data into pairs.mp4 30.4 MB
  228. 6. Converting the data into pairs.srt 5.8 KB
  229. 7. Generating Generators.mp4 32.4 MB
  230. 7. Generating Generators.srt 5.7 KB
  231. 8. Creating the model and loss.mp4 29.4 MB
  232. 8. Creating the model and loss.srt 5.4 KB
  233. 9. Accuracy and imbalanced classes.mp4 51.1 MB
  234. 9. Accuracy and imbalanced classes.srt 9.5 KB
  235. 10. Facial Recognition Section Summary.mp4 18.3 MB
  236. 10. Facial Recognition Section Summary.srt 4.4 KB
  237. 1. Mean Squared Error.mp4 33.8 MB
  238. 1. Mean Squared Error.srt 11.2 KB
  239. 2. Binary Cross Entropy.mp4 23.7 MB
  240. 2. Binary Cross Entropy.srt 7.3 KB
  241. 3. Categorical Cross Entropy.mp4 31.7 MB
  242. 3. Categorical Cross Entropy.srt 9.6 KB
  243. 1. Gradient Descent.mp4 34.9 MB
  244. 1. Gradient Descent.srt 9.8 KB
  245. 2. Stochastic Gradient Descent.mp4 23.0 MB
  246. 2. Stochastic Gradient Descent.srt 5.4 KB
  247. 3. Momentum.mp4 34.2 MB
  248. 3. Momentum.srt 7.8 KB
  249. 4. Variable and Adaptive Learning Rates.mp4 34.9 MB
  250. 4. Variable and Adaptive Learning Rates.srt 15.2 KB
  251. 5. Adam.mp4 38.9 MB
  252. 5. Adam.srt 13.5 KB
  253. 1. Links To Colab Notebooks.html 7.2 KB
  254. 2. Links to VIP Notebooks.html 256 bytes
  255. 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 150.7 MB
  256. 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.7 KB
  257. READ_ME.txt 404 bytes
  258. 2. Windows-Focused Environment Setup 2018.srt 20.0 KB
  259. 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 167.3 MB
  260. 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32.0 KB
  261. 1. What is the Appendix.mp4 16.4 MB
  262. 1. What is the Appendix.srt 3.7 KB
  263. 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 105.7 MB
  264. 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 31.6 KB
  265. 3. How to Code Yourself (part 1).mp4 71.9 MB
  266. 4. How to Code Yourself (part 2).mp4 49.2 MB
  267. 4. How to Code Yourself (part 2).srt 13.0 KB
  268. 5. Proof that using Jupyter Notebook is the same as not using it.mp4 69.5 MB
  269. 5. Proof that using Jupyter Notebook is the same as not using it.srt 14.2 KB
  270. 6. How to Succeed in this Course (Long Version).mp4 35.2 MB
  271. 6. How to Succeed in this Course (Long Version).srt 14.6 KB
  272. 7. What order should I take your courses in (part 1).mp4 79.6 MB
  273. 7. What order should I take your courses in (part 1).srt 16.1 KB
  274. 8. What order should I take your courses in (part 2).mp4 108.2 MB
  275. 8. What order should I take your courses in (part 2).srt 23.0 KB
  276. 9. BONUS Where to get discount coupons and FREE deep learning material.mp4 37.8 MB
  277. 9. BONUS Where to get discount coupons and FREE deep learning material.srt 7.9 KB

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