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GetFreeCourses.Co-Udemy-Tensorflow 2.0 Deep Learning and Artificial Intelligence

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文件列表

  1. 18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.mp4 181MB
  2. 18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 167MB
  3. 18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 151MB
  4. 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 124MB
  5. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 108MB
  6. 20. 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 106MB
  7. 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 105MB
  8. 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 99MB
  9. 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 90MB
  10. 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 87MB
  11. 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 83MB
  12. 5. Convolutional Neural Networks/5. CNN Architecture.mp4 81MB
  13. 4. Feedforward Artificial Neural Networks/5. Activation Functions.mp4 81MB
  14. 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 80MB
  15. 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 80MB
  16. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 80MB
  17. 10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 78MB
  18. 5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 77MB
  19. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 76MB
  20. 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 74MB
  21. 1. Welcome/2. Outline.mp4 74MB
  22. 2. Google Colab/3. Uploading your own data to Google Colab.mp4 74MB
  23. 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 73MB
  24. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp4 72MB
  25. 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 72MB
  26. 4. Feedforward Artificial Neural Networks/7. How to Represent Images.mp4 70MB
  27. 19. 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 69MB
  28. 5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 69MB
  29. 4. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 69MB
  30. 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 69MB
  31. 4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.mp4 69MB
  32. 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 68MB
  33. 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 67MB
  34. 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 67MB
  35. 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 67MB
  36. 3. Machine Learning and Neurons/1. What is Machine Learning.mp4 65MB
  37. 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 65MB
  38. 1. Welcome/3. Where to get the code.mp4 63MB
  39. 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 62MB
  40. 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 60MB
  41. 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 59MB
  42. 14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 59MB
  43. 3. Machine Learning and Neurons/5. Regression Notebook.mp4 57MB
  44. 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 57MB
  45. 4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.mp4 56MB
  46. 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 56MB
  47. 14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 56MB
  48. 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 55MB
  49. 16. In-Depth Gradient Descent/5. Adam (pt 1).mp4 55MB
  50. 3. Machine Learning and Neurons/3. Classification Notebook.mp4 55MB
  51. 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 54MB
  52. 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 53MB
  53. 16. In-Depth Gradient Descent/6. Adam (pt 2).mp4 53MB
  54. 7. Natural Language Processing (NLP)/1. Embeddings.mp4 53MB
  55. 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 53MB
  56. 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 52MB
  57. 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 52MB
  58. 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 51MB
  59. 4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 51MB
  60. 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 51MB
  61. 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 50MB
  62. 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 50MB
  63. 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 49MB
  64. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp4 49MB
  65. 3. Machine Learning and Neurons/7. How does a model learn.mp4 48MB
  66. 4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 48MB
  67. 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 47MB
  68. 4. Feedforward Artificial Neural Networks/3. Forward Propagation.mp4 47MB
  69. 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 46MB
  70. 13. Advanced Tensorflow Usage/6. Using the TPU.mp4 45MB
  71. 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 45MB
  72. 2. Google Colab/5. How to Succeed in this Course.mp4 44MB
  73. 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 44MB
  74. 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 44MB
  75. 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 43MB
  76. 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 43MB
  77. 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 43MB
  78. 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 43MB
  79. 3. Machine Learning and Neurons/6. The Neuron.mp4 43MB
  80. 12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.mp4 42MB
  81. 4. Feedforward Artificial Neural Networks/6. Multiclass Classification.mp4 41MB
  82. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.mp4 41MB
  83. 2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 41MB
  84. 7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 40MB
  85. 14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 40MB
  86. 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 40MB
  87. 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 40MB
  88. 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 40MB
  89. 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 39MB
  90. 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 39MB
  91. 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 38MB
  92. 17. Extras/1. How to Choose Hyperparameters.mp4 38MB
  93. 21. Appendix FAQ Finale/2. BONUS Lecture.mp4 38MB
  94. 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 38MB
  95. 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 37MB
  96. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 35MB
  97. 5. Convolutional Neural Networks/9. Data Augmentation.mp4 35MB
  98. 16. In-Depth Gradient Descent/1. Gradient Descent.mp4 35MB
  99. 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 35MB
  100. 1. Welcome/1. Introduction.mp4 35MB
  101. 16. In-Depth Gradient Descent/3. Momentum.mp4 34MB
  102. 3. Machine Learning and Neurons/8. Making Predictions.mp4 34MB
  103. 15. In-Depth Loss Functions/1. Mean Squared Error.mp4 34MB
  104. 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 33MB
  105. 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 32MB
  106. 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 32MB
  107. 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 32MB
  108. 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 30MB
  109. 3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 30MB
  110. 5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 30MB
  111. 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 29MB
  112. 7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 29MB
  113. 6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp4 28MB
  114. 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 28MB
  115. 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 28MB
  116. 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 27MB
  117. 3. Machine Learning and Neurons/11. Suggestion Box.mp4 27MB
  118. 3. Machine Learning and Neurons/10. Why Keras.mp4 27MB
  119. 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 26MB
  120. 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 26MB
  121. 17. Extras/2. Where Are The Exercises.mp4 26MB
  122. 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 24MB
  123. 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 24MB
  124. 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 23MB
  125. 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 23MB
  126. 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 22MB
  127. 11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 21MB
  128. 5. Convolutional Neural Networks/10. Batch Normalization.mp4 21MB
  129. 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 21MB
  130. 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 18MB
  131. 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 17MB
  132. 21. Appendix FAQ Finale/1. What is the Appendix.mp4 16MB
  133. 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 16MB
  134. 18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32KB
  135. 20. 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
  136. 5. Convolutional Neural Networks/5. CNN Architecture.srt 28KB
  137. 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.srt 26KB
  138. 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.srt 26KB
  139. 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.srt 24KB
  140. 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.srt 23KB
  141. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23KB
  142. 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).srt 23KB
  143. 4. Feedforward Artificial Neural Networks/5. Activation Functions.srt 23KB
  144. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).srt 22KB
  145. 10. GANs (Generative Adversarial Networks)/1. GAN Theory.srt 21KB
  146. 5. Convolutional Neural Networks/4. Convolution on Color Images.srt 21KB
  147. 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.srt 20KB
  148. 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).srt 20KB
  149. 5. Convolutional Neural Networks/1. What is Convolution (part 1).srt 20KB
  150. 18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.srt 20KB
  151. 5. Convolutional Neural Networks/6. CNN Code Preparation.srt 20KB
  152. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.srt 19KB
  153. 3. Machine Learning and Neurons/1. What is Machine Learning.srt 18KB
  154. 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.srt 18KB
  155. 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.srt 17KB
  156. 1. Welcome/2. Outline.srt 17KB
  157. 4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.srt 17KB
  158. 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).srt 17KB
  159. 16. In-Depth Gradient Descent/5. Adam (pt 1).srt 17KB
  160. 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).srt 16KB
  161. 4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).srt 16KB
  162. 7. Natural Language Processing (NLP)/1. Embeddings.srt 16KB
  163. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16KB
  164. 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).srt 16KB
  165. 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.srt 16KB
  166. 4. Feedforward Artificial Neural Networks/7. How to Represent Images.srt 16KB
  167. 1. Welcome/3. Where to get the code.srt 15KB
  168. 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.srt 15KB
  169. 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15KB
  170. 10. GANs (Generative Adversarial Networks)/2. GAN Code.srt 15KB
  171. 18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15KB
  172. 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 15KB
  173. 16. In-Depth Gradient Descent/6. Adam (pt 2).srt 14KB
  174. 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).srt 14KB
  175. 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).srt 14KB
  176. 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.srt 14KB
  177. 19. 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
  178. 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14KB
  179. 3. Machine Learning and Neurons/7. How does a model learn.srt 14KB
  180. 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).srt 14KB
  181. 14. Low-Level Tensorflow/3. Variables and Gradient Tape.srt 14KB
  182. 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.srt 13KB
  183. 14. Low-Level Tensorflow/4. Build Your Own Custom Model.srt 13KB
  184. 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).srt 13KB
  185. 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.srt 13KB
  186. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).srt 13KB
  187. 4. Feedforward Artificial Neural Networks/10. ANN for Regression.srt 13KB
  188. 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).srt 13KB
  189. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.srt 13KB
  190. 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.srt 13KB
  191. 3. Machine Learning and Neurons/6. The Neuron.srt 12KB
  192. 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12KB
  193. 4. Feedforward Artificial Neural Networks/3. Forward Propagation.srt 12KB
  194. 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt 12KB
  195. 3. Machine Learning and Neurons/5. Regression Notebook.srt 12KB
  196. 2. Google Colab/3. Uploading your own data to Google Colab.srt 12KB
  197. 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.srt 12KB
  198. 12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.srt 12KB
  199. 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.srt 12KB
  200. 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 12KB
  201. 4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.srt 12KB
  202. 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.srt 11KB
  203. 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.srt 11KB
  204. 5. Convolutional Neural Networks/9. Data Augmentation.srt 11KB
  205. 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.srt 11KB
  206. 15. In-Depth Loss Functions/1. Mean Squared Error.srt 11KB
  207. 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).srt 11KB
  208. 4. Feedforward Artificial Neural Networks/6. Multiclass Classification.srt 11KB
  209. 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.srt 11KB
  210. 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).srt 10KB
  211. 7. Natural Language Processing (NLP)/5. CNNs for Text.srt 10KB
  212. 4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.srt 10KB
  213. 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.srt 10KB
  214. 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.srt 10KB
  215. 16. In-Depth Gradient Descent/1. Gradient Descent.srt 10KB
  216. 14. Low-Level Tensorflow/2. Constants and Basic Computation.srt 10KB
  217. 15. In-Depth Loss Functions/3. Categorical Cross Entropy.srt 10KB
  218. 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.srt 10KB
  219. 2. Google Colab/2. Tensorflow 2.0 in Google Colab.srt 9KB
  220. 3. Machine Learning and Neurons/3. Classification Notebook.srt 9KB
  221. 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).srt 9KB
  222. 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.srt 9KB
  223. 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.srt 9KB
  224. 17. Extras/1. How to Choose Hyperparameters.srt 9KB
  225. 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.srt 9KB
  226. 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.srt 9KB
  227. 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.srt 9KB
  228. 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.srt 8KB
  229. 2. Google Colab/5. How to Succeed in this Course.srt 8KB
  230. 17. Extras/3. Links to TF2.0 Notebooks.html 8KB
  231. 5. Convolutional Neural Networks/3. What is Convolution (part 3).srt 8KB
  232. 3. Machine Learning and Neurons/8. Making Predictions.srt 8KB
  233. 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.srt 8KB
  234. 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.srt 8KB
  235. 21. Appendix FAQ Finale/2. BONUS Lecture.srt 8KB
  236. 16. In-Depth Gradient Descent/3. Momentum.srt 8KB
  237. 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.srt 8KB
  238. 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).srt 8KB
  239. 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.srt 8KB
  240. 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.srt 7KB
  241. 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt 7KB
  242. 15. In-Depth Loss Functions/2. Binary Cross Entropy.srt 7KB
  243. 5. Convolutional Neural Networks/2. What is Convolution (part 2).srt 7KB
  244. 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.srt 7KB
  245. 6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.srt 7KB
  246. 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.srt 7KB
  247. 13. Advanced Tensorflow Usage/6. Using the TPU.srt 7KB
  248. 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.srt 7KB
  249. 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.srt 7KB
  250. 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.srt 7KB
  251. 5. Convolutional Neural Networks/10. Batch Normalization.srt 7KB
  252. 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).srt 6KB
  253. 11. Deep Reinforcement Learning (Theory)/5. The Return.srt 6KB
  254. 7. Natural Language Processing (NLP)/3. Text Preprocessing.srt 6KB
  255. 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).srt 6KB
  256. 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.srt 6KB
  257. 3. Machine Learning and Neurons/10. Why Keras.srt 6KB
  258. 1. Welcome/1. Introduction.srt 6KB
  259. 17. Extras/2. Where Are The Exercises.srt 5KB
  260. 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.srt 5KB
  261. 5. Convolutional Neural Networks/8. CNN for CIFAR-10.srt 5KB
  262. 3. Machine Learning and Neurons/9. Saving and Loading a Model.srt 5KB
  263. 3. Machine Learning and Neurons/11. Suggestion Box.srt 5KB
  264. 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.srt 5KB
  265. 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.srt 4KB
  266. 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).srt 4KB
  267. 21. Appendix FAQ Finale/1. What is the Appendix.srt 4KB
  268. 13. Advanced Tensorflow Usage/How you can help GetFreeCourses.Co.txt 182B
  269. 5. Convolutional Neural Networks/How you can help GetFreeCourses.Co.txt 182B
  270. How you can help GetFreeCourses.Co.txt 182B
  271. 1. Welcome/3.1 Colab Notebooks.html 157B
  272. 1. Welcome/3.2 Github Link.html 120B
  273. 13. Advanced Tensorflow Usage/GetFreeCourses.Co.url 116B
  274. 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/GetFreeCourses.Co.url 116B
  275. 5. Convolutional Neural Networks/GetFreeCourses.Co.url 116B
  276. Download Paid Udemy Courses For Free.url 116B
  277. GetFreeCourses.Co.url 116B