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[] Udemy - PyTorch Deep Learning and Artificial Intelligence

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文件列表

  1. 19. Setting up your Environment (FAQ by Student Request)/3. Anaconda Environment Setup.mp4 348MB
  2. 19. Setting up your Environment (FAQ by Student Request)/4. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 217MB
  3. 19. Setting up your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 210MB
  4. 8. Natural Language Processing (NLP)/7. Text Classification with LSTMs (V2).mp4 177MB
  5. 7. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 161MB
  6. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 150MB
  7. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 147MB
  8. 5. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 142MB
  9. 4. Machine Learning and Neurons/9. Classification Notebook.mp4 140MB
  10. 9. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).mp4 127MB
  11. 3. Google Colab/2. Uploading your own data to Google Colab.mp4 127MB
  12. 4. Machine Learning and Neurons/6. Moore's Law Notebook.mp4 127MB
  13. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp4 119MB
  14. 7. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 110MB
  15. 5. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 104MB
  16. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp4 104MB
  17. 10. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 100MB
  18. 9. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).mp4 97MB
  19. 7. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 92MB
  20. 7. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).mp4 90MB
  21. 8. Natural Language Processing (NLP)/10. (Legacy) VIP Making Predictions with a Trained NLP Model.mp4 88MB
  22. 13. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 83MB
  23. 11. GANs (Generative Adversarial Networks)/3. GAN Code.mp4 82MB
  24. 6. Convolutional Neural Networks/6. CNN Code Preparation (part 1).mp4 80MB
  25. 7. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 80MB
  26. 6. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 80MB
  27. 10. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 77MB
  28. 6. Convolutional Neural Networks/13. Improving CIFAR-10 Results.mp4 76MB
  29. 6. Convolutional Neural Networks/4. Convolution on Color Images.mp4 76MB
  30. 6. Convolutional Neural Networks/9. CNN for Fashion MNIST.mp4 74MB
  31. 8. Natural Language Processing (NLP)/4. Beginner Blues - PyTorch NLP Version.mp4 73MB
  32. 10. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 73MB
  33. 7. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 71MB
  34. 4. Machine Learning and Neurons/4. Regression Notebook.mp4 71MB
  35. 7. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).mp4 70MB
  36. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 69MB
  37. 11. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 69MB
  38. 5. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 66MB
  39. 4. Machine Learning and Neurons/7. Linear Classification Basics.mp4 66MB
  40. 12. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 65MB
  41. 22. Appendix FAQ Finale/2. BONUS.mp4 65MB
  42. 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).mp4 64MB
  43. 15. VIP Facial Recognition/7. Generating Generators.mp4 60MB
  44. 7. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 59MB
  45. 8. Natural Language Processing (NLP)/8. CNNs for Text.mp4 58MB
  46. 13. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 58MB
  47. 6. Convolutional Neural Networks/5. CNN Architecture.mp4 58MB
  48. 4. Machine Learning and Neurons/2. Regression Basics.mp4 58MB
  49. 3. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 57MB
  50. 3. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 56MB
  51. 12. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 56MB
  52. 5. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 55MB
  53. 6. Convolutional Neural Networks/10. CNN for CIFAR-10.mp4 55MB
  54. 17. In-Depth Gradient Descent/5. Adam (pt 1).mp4 55MB
  55. 7. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).mp4 54MB
  56. 4. Machine Learning and Neurons/1. What is Machine Learning.mp4 54MB
  57. 13. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 54MB
  58. 7. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 53MB
  59. 13. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 53MB
  60. 7. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 50MB
  61. 4. Machine Learning and Neurons/10. Saving and Loading a Model.mp4 50MB
  62. 15. VIP Facial Recognition/4. Loading in the data.mp4 49MB
  63. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 49MB
  64. 4. Machine Learning and Neurons/13. Model With Logits.mp4 48MB
  65. 7. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 48MB
  66. 1. Introduction/2. Overview and Outline.mp4 48MB
  67. 8. Natural Language Processing (NLP)/9. Text Classification with CNNs (V2).mp4 48MB
  68. 9. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 46MB
  69. 8. Natural Language Processing (NLP)/6. (Legacy) Text Preprocessing Code Example.mp4 46MB
  70. 12. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 46MB
  71. 15. VIP Facial Recognition/9. Accuracy and imbalanced classes.mp4 46MB
  72. 12. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 45MB
  73. 5. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 45MB
  74. 9. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.mp4 45MB
  75. 12. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 42MB
  76. 17. In-Depth Gradient Descent/6. Adam (pt 2).mp4 42MB
  77. 15. VIP Facial Recognition/6. Converting the data into pairs.mp4 42MB
  78. 12. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 39MB
  79. 7. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 39MB
  80. 8. Natural Language Processing (NLP)/1. Embeddings.mp4 37MB
  81. 13. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 37MB
  82. 8. Natural Language Processing (NLP)/3. Text Preprocessing Concepts.mp4 35MB
  83. 14. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.mp4 35MB
  84. 4. Machine Learning and Neurons/14. Train Sets vs. Validation Sets vs. Test Sets.mp4 34MB
  85. 2. Getting Set Up/5. Temporary 403 Errors.mp4 34MB
  86. 12. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 33MB
  87. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp4 33MB
  88. 4. Machine Learning and Neurons/12. How does a model learn.mp4 33MB
  89. 9. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.mp4 33MB
  90. 5. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 33MB
  91. 8. Natural Language Processing (NLP)/11. VIP Making Predictions with a Trained NLP Model (V2).mp4 33MB
  92. 12. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 32MB
  93. 4. Machine Learning and Neurons/3. Regression Code Preparation.mp4 31MB
  94. 5. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 31MB
  95. 15. VIP Facial Recognition/2. Siamese Networks.mp4 31MB
  96. 12. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 31MB
  97. 8. Natural Language Processing (NLP)/5. (Legacy) Text Preprocessing Code Preparation.mp4 31MB
  98. 4. Machine Learning and Neurons/11. A Short Neuroscience Primer.mp4 30MB
  99. 6. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 30MB
  100. 5. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 30MB
  101. 5. Feedforward Artificial Neural Networks/11. How to Choose Hyperparameters.mp4 30MB
  102. 12. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 29MB
  103. 12. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 29MB
  104. 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 29MB
  105. 1. Introduction/1. Welcome.mp4 29MB
  106. 6. Convolutional Neural Networks/11. Data Augmentation.mp4 29MB
  107. 17. In-Depth Gradient Descent/3. Momentum.mp4 29MB
  108. 14. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.mp4 28MB
  109. 12. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 28MB
  110. 17. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 28MB
  111. 17. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 27MB
  112. 4. Machine Learning and Neurons/5. Moore's Law.mp4 27MB
  113. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 27MB
  114. 4. Machine Learning and Neurons/15. Suggestion Box.mp4 27MB
  115. 15. VIP Facial Recognition/8. Creating the model and loss.mp4 27MB
  116. 7. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).mp4 26MB
  117. 10. Transfer Learning for Computer Vision/3. Large Datasets.mp4 25MB
  118. 16. In-Depth Loss Functions/1. Mean Squared Error.mp4 24MB
  119. 6. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 24MB
  120. 6. Convolutional Neural Networks/7. CNN Code Preparation (part 2).mp4 24MB
  121. 17. In-Depth Gradient Descent/1. Gradient Descent.mp4 24MB
  122. 12. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 23MB
  123. 16. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 23MB
  124. 15. VIP Facial Recognition/5. Splitting the data into train and test.mp4 23MB
  125. 7. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).mp4 20MB
  126. 5. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 20MB
  127. 6. Convolutional Neural Networks/8. CNN Code Preparation (part 3).mp4 20MB
  128. 18. Extras/1. Where Are The Exercises.mp4 20MB
  129. 4. Machine Learning and Neurons/8. Classification Code Preparation.mp4 18MB
  130. 13. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 18MB
  131. 7. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.mp4 18MB
  132. 11. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.mp4 18MB
  133. 10. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 18MB
  134. 13. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 18MB
  135. 2. Getting Set Up/3. Where to get the code, notebooks, and data.mp4 18MB
  136. 10. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 17MB
  137. 2. Getting Set Up/4. How to Succeed in This Course.mp4 16MB
  138. 13. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 16MB
  139. 19. Setting up your Environment (FAQ by Student Request)/1. Pre-Installation Check.mp4 15MB
  140. 16. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 15MB
  141. 6. Convolutional Neural Networks/12. Batch Normalization.mp4 15MB
  142. 12. Deep Reinforcement Learning (Theory)/5. The Return.mp4 14MB
  143. 15. VIP Facial Recognition/3. Code Outline.mp4 14MB
  144. 15. VIP Facial Recognition/1. Facial Recognition Section Introduction.mp4 14MB
  145. 7. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 11MB
  146. 15. VIP Facial Recognition/10. Facial Recognition Section Summary.mp4 11MB
  147. 8. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.mp4 11MB
  148. 13. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 10MB
  149. 22. Appendix FAQ Finale/1. What is the Appendix.mp4 10MB
  150. 5. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.mp4 3MB
  151. 19. Setting up your Environment (FAQ by Student Request)/4. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32KB
  152. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32KB
  153. 7. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.srt 30KB
  154. 6. Convolutional Neural Networks/5. CNN Architecture.srt 28KB
  155. 12. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.srt 26KB
  156. 7. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.srt 26KB
  157. 6. Convolutional Neural Networks/6. CNN Code Preparation (part 1).srt 24KB
  158. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).srt 23KB
  159. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23KB
  160. 7. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).srt 23KB
  161. 5. Feedforward Artificial Neural Networks/4. Activation Functions.srt 23KB
  162. 5. Feedforward Artificial Neural Networks/9. ANN for Image Classification.srt 23KB
  163. 6. Convolutional Neural Networks/1. What is Convolution (part 1).srt 21KB
  164. 11. GANs (Generative Adversarial Networks)/1. GAN Theory.srt 21KB
  165. 6. Convolutional Neural Networks/4. Convolution on Color Images.srt 21KB
  166. 4. Machine Learning and Neurons/7. Linear Classification Basics.srt 21KB
  167. 5. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).srt 20KB
  168. 8. Natural Language Processing (NLP)/7. Text Classification with LSTMs (V2).srt 20KB
  169. 4. Machine Learning and Neurons/2. Regression Basics.srt 20KB
  170. 19. Setting up your Environment (FAQ by Student Request)/3. Anaconda Environment Setup.srt 20KB
  171. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.srt 19KB
  172. 4. Machine Learning and Neurons/1. What is Machine Learning.srt 18KB
  173. 12. Deep Reinforcement Learning (Theory)/11. Q-Learning.srt 18KB
  174. 8. Natural Language Processing (NLP)/3. Text Preprocessing Concepts.srt 18KB
  175. 1. Introduction/2. Overview and Outline.srt 18KB
  176. 7. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.srt 18KB
  177. 4. Machine Learning and Neurons/4. Regression Notebook.srt 17KB
  178. 9. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).srt 17KB
  179. 17. In-Depth Gradient Descent/5. Adam (pt 1).srt 17KB
  180. 12. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).srt 16KB
  181. 4. Machine Learning and Neurons/3. Regression Code Preparation.srt 16KB
  182. 8. Natural Language Processing (NLP)/1. Embeddings.srt 16KB
  183. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16KB
  184. 8. Natural Language Processing (NLP)/8. CNNs for Text.srt 16KB
  185. 7. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).srt 16KB
  186. 4. Machine Learning and Neurons/6. Moore's Law Notebook.srt 16KB
  187. 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).srt 16KB
  188. 13. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.srt 16KB
  189. 12. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15KB
  190. 3. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 15KB
  191. 8. Natural Language Processing (NLP)/5. (Legacy) Text Preprocessing Code Preparation.srt 15KB
  192. 5. Feedforward Artificial Neural Networks/6. How to Represent Images.srt 15KB
  193. 17. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.srt 15KB
  194. 7. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).srt 15KB
  195. 19. Setting up your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15KB
  196. 7. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.srt 15KB
  197. 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 15KB
  198. 4. Machine Learning and Neurons/9. Classification Notebook.srt 15KB
  199. 8. Natural Language Processing (NLP)/4. Beginner Blues - PyTorch NLP Version.srt 15KB
  200. 17. In-Depth Gradient Descent/6. Adam (pt 2).srt 14KB
  201. 3. Google Colab/2. Uploading your own data to Google Colab.srt 14KB
  202. 7. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).srt 14KB
  203. 3. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14KB
  204. 4. Machine Learning and Neurons/14. Train Sets vs. Validation Sets vs. Test Sets.srt 14KB
  205. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.srt 14KB
  206. 7. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.srt 14KB
  207. 4. Machine Learning and Neurons/12. How does a model learn.srt 14KB
  208. 9. Recommender Systems/1. Recommender Systems with Deep Learning Theory.srt 14KB
  209. 6. Convolutional Neural Networks/9. CNN for Fashion MNIST.srt 13KB
  210. 12. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).srt 13KB
  211. 5. Feedforward Artificial Neural Networks/10. ANN for Regression.srt 13KB
  212. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).srt 13KB
  213. 12. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 13KB
  214. 15. VIP Facial Recognition/2. Siamese Networks.srt 13KB
  215. 6. Convolutional Neural Networks/13. Improving CIFAR-10 Results.srt 13KB
  216. 14. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.srt 13KB
  217. 9. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.srt 13KB
  218. 12. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).srt 13KB
  219. 6. Convolutional Neural Networks/11. Data Augmentation.srt 13KB
  220. 12. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.srt 13KB
  221. 4. Machine Learning and Neurons/11. A Short Neuroscience Primer.srt 12KB
  222. 5. Feedforward Artificial Neural Networks/2. Forward Propagation.srt 12KB
  223. 5. Feedforward Artificial Neural Networks/5. Multiclass Classification.srt 12KB
  224. 13. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.srt 12KB
  225. 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt 12KB
  226. 13. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.srt 12KB
  227. 10. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).srt 12KB
  228. 5. Feedforward Artificial Neural Networks/3. The Geometrical Picture.srt 12KB
  229. 12. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.srt 11KB
  230. 16. In-Depth Loss Functions/1. Mean Squared Error.srt 11KB
  231. 7. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.srt 11KB
  232. 9. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).srt 11KB
  233. 10. Transfer Learning for Computer Vision/1. Transfer Learning Theory.srt 11KB
  234. 7. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.srt 11KB
  235. 11. GANs (Generative Adversarial Networks)/3. GAN Code.srt 11KB
  236. 6. Convolutional Neural Networks/7. CNN Code Preparation (part 2).srt 10KB
  237. 7. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.srt 10KB
  238. 17. In-Depth Gradient Descent/1. Gradient Descent.srt 10KB
  239. 16. In-Depth Loss Functions/3. Categorical Cross Entropy.srt 10KB
  240. 15. VIP Facial Recognition/9. Accuracy and imbalanced classes.srt 10KB
  241. 8. Natural Language Processing (NLP)/6. (Legacy) Text Preprocessing Code Example.srt 9KB
  242. 4. Machine Learning and Neurons/8. Classification Code Preparation.srt 9KB
  243. 4. Machine Learning and Neurons/5. Moore's Law.srt 9KB
  244. 8. Natural Language Processing (NLP)/10. (Legacy) VIP Making Predictions with a Trained NLP Model.srt 9KB
  245. 10. Transfer Learning for Computer Vision/3. Large Datasets.srt 9KB
  246. 6. Convolutional Neural Networks/10. CNN for CIFAR-10.srt 9KB
  247. 12. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.srt 9KB
  248. 14. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.srt 9KB
  249. 10. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).srt 9KB
  250. 5. Feedforward Artificial Neural Networks/11. How to Choose Hyperparameters.srt 9KB
  251. 13. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.srt 9KB
  252. 12. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.srt 9KB
  253. 11. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.srt 9KB
  254. 13. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.srt 8KB
  255. 13. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.srt 8KB
  256. 6. Convolutional Neural Networks/3. What is Convolution (part 3).srt 8KB
  257. 5. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.srt 8KB
  258. 17. In-Depth Gradient Descent/3. Momentum.srt 8KB
  259. 22. Appendix FAQ Finale/2. BONUS.srt 8KB
  260. 12. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.srt 8KB
  261. 12. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.srt 7KB
  262. 16. In-Depth Loss Functions/2. Binary Cross Entropy.srt 7KB
  263. 6. Convolutional Neural Networks/2. What is Convolution (part 2).srt 7KB
  264. 6. Convolutional Neural Networks/8. CNN Code Preparation (part 3).srt 7KB
  265. 7. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.srt 7KB
  266. 8. Natural Language Processing (NLP)/9. Text Classification with CNNs (V2).srt 7KB
  267. 13. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.srt 7KB
  268. 15. VIP Facial Recognition/4. Loading in the data.srt 7KB
  269. 7. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).srt 7KB
  270. 13. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.srt 7KB
  271. 4. Machine Learning and Neurons/10. Saving and Loading a Model.srt 7KB
  272. 19. Setting up your Environment (FAQ by Student Request)/1. Pre-Installation Check.srt 7KB
  273. 6. Convolutional Neural Networks/12. Batch Normalization.srt 7KB
  274. 12. Deep Reinforcement Learning (Theory)/5. The Return.srt 6KB
  275. 9. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.srt 6KB
  276. 7. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).srt 6KB
  277. 10. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.srt 6KB
  278. 15. VIP Facial Recognition/3. Code Outline.srt 6KB
  279. 15. VIP Facial Recognition/6. Converting the data into pairs.srt 6KB
  280. 15. VIP Facial Recognition/7. Generating Generators.srt 6KB
  281. 1. Introduction/1. Welcome.srt 6KB
  282. 18. Extras/1. Where Are The Exercises.srt 5KB
  283. 17. In-Depth Gradient Descent/2. Stochastic Gradient Descent.srt 5KB
  284. 15. VIP Facial Recognition/8. Creating the model and loss.srt 5KB
  285. 8. Natural Language Processing (NLP)/11. VIP Making Predictions with a Trained NLP Model (V2).srt 5KB
  286. 4. Machine Learning and Neurons/13. Model With Logits.srt 5KB
  287. 10. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt 5KB
  288. 15. VIP Facial Recognition/5. Splitting the data into train and test.srt 5KB
  289. 4. Machine Learning and Neurons/15. Suggestion Box.srt 5KB
  290. 15. VIP Facial Recognition/1. Facial Recognition Section Introduction.srt 5KB
  291. 7. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.srt 5KB
  292. 8. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.srt 5KB
  293. 2. Getting Set Up/4. How to Succeed in This Course.srt 4KB
  294. 15. VIP Facial Recognition/10. Facial Recognition Section Summary.srt 4KB
  295. 13. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.srt 4KB
  296. 2. Getting Set Up/3. Where to get the code, notebooks, and data.srt 4KB
  297. 22. Appendix FAQ Finale/1. What is the Appendix.srt 4KB
  298. 2. Getting Set Up/5. Temporary 403 Errors.srt 4KB
  299. 7. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).srt 3KB
  300. 5. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.srt 1KB
  301. 2. Getting Set Up/1.1 Data Links.html 157B
  302. 2. Getting Set Up/3.2 Data Links.html 157B
  303. 2. Getting Set Up/1.2 Github Link.html 140B
  304. 2. Getting Set Up/3.3 Github Link.html 140B
  305. 0. Websites you may like/[FreeCourseSite.com].url 127B
  306. 13. Stock Trading Project with Deep Reinforcement Learning/0. Websites you may like/[FreeCourseSite.com].url 127B
  307. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/0. Websites you may like/[FreeCourseSite.com].url 127B
  308. 3. Google Colab/0. Websites you may like/[FreeCourseSite.com].url 127B
  309. 5. Feedforward Artificial Neural Networks/0. Websites you may like/[FreeCourseSite.com].url 127B
  310. 7. Recurrent Neural Networks, Time Series, and Sequence Data/0. Websites you may like/[FreeCourseSite.com].url 127B
  311. 9. Recommender Systems/0. Websites you may like/[FreeCourseSite.com].url 127B
  312. 2. Getting Set Up/3.1 Code Link.html 125B
  313. 0. Websites you may like/[CourseClub.Me].url 122B
  314. 13. Stock Trading Project with Deep Reinforcement Learning/0. Websites you may like/[CourseClub.Me].url 122B
  315. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/0. Websites you may like/[CourseClub.Me].url 122B
  316. 3. Google Colab/0. Websites you may like/[CourseClub.Me].url 122B
  317. 5. Feedforward Artificial Neural Networks/0. Websites you may like/[CourseClub.Me].url 122B
  318. 7. Recurrent Neural Networks, Time Series, and Sequence Data/0. Websites you may like/[CourseClub.Me].url 122B
  319. 9. Recommender Systems/0. Websites you may like/[CourseClub.Me].url 122B
  320. 8. Natural Language Processing (NLP)/4.1 Why bad programmers always need the latest version.html 89B
  321. 0. Websites you may like/[GigaCourse.Com].url 49B
  322. 13. Stock Trading Project with Deep Reinforcement Learning/0. Websites you may like/[GigaCourse.Com].url 49B
  323. 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/0. Websites you may like/[GigaCourse.Com].url 49B
  324. 3. Google Colab/0. Websites you may like/[GigaCourse.Com].url 49B
  325. 5. Feedforward Artificial Neural Networks/0. Websites you may like/[GigaCourse.Com].url 49B
  326. 7. Recurrent Neural Networks, Time Series, and Sequence Data/0. Websites you may like/[GigaCourse.Com].url 49B
  327. 9. Recommender Systems/0. Websites you may like/[GigaCourse.Com].url 49B