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[] Udemy - A deep understanding of deep learning (with Python intro)

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  • 文件大小:16GB
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文件列表

  1. 19 - Understand and design CNNs/005 Examine feature map activations.mp4 251MB
  2. 22 - Style transfer/004 Transferring the screaming bathtub.mp4 210MB
  3. 19 - Understand and design CNNs/004 Classify Gaussian blurs.mp4 176MB
  4. 07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison.mp4 169MB
  5. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation.mp4 167MB
  6. 18 - Convolution and transformations/003 Convolution in code.mp4 166MB
  7. 14 - FFN milestone projects/004 Project 2 My solution.mp4 156MB
  8. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences.mp4 154MB
  9. 19 - Understand and design CNNs/002 CNN to classify MNIST digits.mp4 145MB
  10. 19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 144MB
  11. 07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset).mp4 142MB
  12. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum.mp4 142MB
  13. 16 - Autoencoders/004 AEs for occlusion.mp4 138MB
  14. 26 - Where to go from here/002 How to read academic DL papers.mp4 137MB
  15. 19 - Understand and design CNNs/011 Discover the Gaussian parameters.mp4 137MB
  16. 21 - Transfer learning/007 Pretraining with autoencoders.mp4 136MB
  17. 16 - Autoencoders/006 Autoencoder with tied weights.mp4 132MB
  18. 23 - Generative adversarial networks/004 CNN GAN with Gaussians.mp4 131MB
  19. 09 - Regularization/004 Dropout regularization in practice.mp4 131MB
  20. 07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties.mp4 130MB
  21. 19 - Understand and design CNNs/008 Do autoencoders clean Gaussians.mp4 129MB
  22. 21 - Transfer learning/005 Transfer learning with ResNet-18.mp4 128MB
  23. 18 - Convolution and transformations/011 Image transforms.mp4 125MB
  24. 10 - Metaparameters (activations, optimizers)/002 The wine quality dataset.mp4 125MB
  25. 23 - Generative adversarial networks/002 Linear GAN with MNIST.mp4 122MB
  26. 08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 121MB
  27. 12 - More on data/003 CodeChallenge unbalanced data.mp4 118MB
  28. 16 - Autoencoders/005 The latent code of MNIST.mp4 118MB
  29. 11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits.mp4 117MB
  30. 07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth.mp4 115MB
  31. 12 - More on data/007 Data feature augmentation.mp4 114MB
  32. 19 - Understand and design CNNs/006 CodeChallenge Softcode internal parameters.mp4 114MB
  33. 15 - Weight inits and investigations/006 CodeChallenge Xavier vs. Kaiming.mp4 109MB
  34. 21 - Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 109MB
  35. 15 - Weight inits and investigations/009 Learning-related changes in weights.mp4 108MB
  36. 08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4 106MB
  37. 07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN.mp4 105MB
  38. 09 - Regularization/003 Dropout regularization.mp4 104MB
  39. 10 - Metaparameters (activations, optimizers)/003 CodeChallenge Minibatch size in the wine dataset.mp4 104MB
  40. 13 - Measuring model performance/004 APRF example 1 wine quality.mp4 103MB
  41. 18 - Convolution and transformations/012 Creating and using custom DataLoaders.mp4 102MB
  42. 10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4 102MB
  43. 07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes.mp4 101MB
  44. 12 - More on data/001 Anatomy of a torch dataset and dataloader.mp4 101MB
  45. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM.mp4 100MB
  46. 16 - Autoencoders/003 CodeChallenge How many units.mp4 100MB
  47. 06 - Gradient descent/007 Parametric experiments on g.d.mp4 99MB
  48. 19 - Understand and design CNNs/010 CodeChallenge Custom loss functions.mp4 99MB
  49. 07 - ANNs (Artificial Neural Networks)/016 Depth vs. breadth number of parameters.mp4 98MB
  50. 12 - More on data/002 Data size and network size.mp4 97MB
  51. 06 - Gradient descent/005 Gradient descent in 2D.mp4 96MB
  52. 15 - Weight inits and investigations/005 Xavier and Kaiming initializations.mp4 96MB
  53. 13 - Measuring model performance/005 APRF example 2 MNIST.mp4 94MB
  54. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/006 More on RNNs Hidden states, embeddings.mp4 94MB
  55. 19 - Understand and design CNNs/007 CodeChallenge How wide the FC.mp4 91MB
  56. 11 - FFNs (Feed-Forward Networks)/007 CodeChallenge MNIST and breadth vs. depth.mp4 90MB
  57. 12 - More on data/010 Save the best-performing model.mp4 90MB
  58. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch.mp4 90MB
  59. 10 - Metaparameters (activations, optimizers)/013 CodeChallenge Predict sugar.mp4 89MB
  60. 12 - More on data/005 Data oversampling in MNIST.mp4 89MB
  61. 11 - FFNs (Feed-Forward Networks)/002 The MNIST dataset.mp4 89MB
  62. 18 - Convolution and transformations/001 Convolution concepts.mp4 88MB
  63. 15 - Weight inits and investigations/008 Freezing weights during learning.mp4 88MB
  64. 06 - Gradient descent/003 Gradient descent in 1D.mp4 88MB
  65. 03 - Concepts in deep learning/003 The role of DL in science and knowledge.mp4 88MB
  66. 16 - Autoencoders/002 Denoising MNIST.mp4 86MB
  67. 15 - Weight inits and investigations/002 A surprising demo of weight initializations.mp4 86MB
  68. 10 - Metaparameters (activations, optimizers)/009 Activation functions.mp4 85MB
  69. 21 - Transfer learning/003 CodeChallenge letters to numbers.mp4 85MB
  70. 11 - FFNs (Feed-Forward Networks)/006 Distributions of weights pre- and post-learning.mp4 85MB
  71. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/008 The LSTM and GRU classes.mp4 84MB
  72. 06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate.mp4 84MB
  73. 09 - Regularization/012 CodeChallenge Effects of mini-batch size.mp4 83MB
  74. 07 - ANNs (Artificial Neural Networks)/014 CodeChallenge more qwerties!.mp4 82MB
  75. 20 - CNN milestone projects/002 Project 1 My solution.mp4 81MB
  76. 19 - Understand and design CNNs/009 CodeChallenge AEs and occluded Gaussians.mp4 79MB
  77. 09 - Regularization/007 L2 regularization in practice.mp4 79MB
  78. 21 - Transfer learning/002 Transfer learning MNIST - FMNIST.mp4 78MB
  79. 30 - Python intro Flow control/010 Function error checking and handling.mp4 77MB
  80. 20 - CNN milestone projects/005 Project 4 Psychometric functions in CNNs.mp4 76MB
  81. 09 - Regularization/010 Batch training in action.mp4 76MB
  82. 12 - More on data/006 Data noise augmentation (with devset+test).mp4 76MB
  83. 18 - Convolution and transformations/005 The Conv2 class in PyTorch.mp4 76MB
  84. 03 - Concepts in deep learning/004 Running experiments to understand DL.mp4 75MB
  85. 07 - ANNs (Artificial Neural Networks)/006 ANN for regression.mp4 74MB
  86. 15 - Weight inits and investigations/003 Theory Why and how to initialize weights.mp4 74MB
  87. 15 - Weight inits and investigations/004 CodeChallenge Weight variance inits.mp4 73MB
  88. 10 - Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4 72MB
  89. 31 - Python intro Text and plots/006 Images.mp4 71MB
  90. 11 - FFNs (Feed-Forward Networks)/005 CodeChallenge Data normalization.mp4 71MB
  91. 09 - Regularization/008 L1 regularization in practice.mp4 71MB
  92. 19 - Understand and design CNNs/013 Dropout in CNNs.mp4 71MB
  93. 10 - Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4 71MB
  94. 13 - Measuring model performance/007 Computation time.mp4 70MB
  95. 08 - Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4 70MB
  96. 05 - Math, numpy, PyTorch/009 Softmax.mp4 70MB
  97. 14 - FFN milestone projects/002 Project 1 My solution.mp4 70MB
  98. 18 - Convolution and transformations/007 Transpose convolution.mp4 69MB
  99. 10 - Metaparameters (activations, optimizers)/023 Learning rate decay.mp4 69MB
  100. 10 - Metaparameters (activations, optimizers)/014 Loss functions.mp4 69MB
  101. 07 - ANNs (Artificial Neural Networks)/015 Comparing the number of hidden units.mp4 68MB
  102. 19 - Understand and design CNNs/015 CodeChallenge Varying number of channels.mp4 67MB
  103. 10 - Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4 67MB
  104. 22 - Style transfer/002 The Gram matrix (feature activation covariance).mp4 66MB
  105. 07 - ANNs (Artificial Neural Networks)/017 Defining models using sequential vs. class.mp4 66MB
  106. 15 - Weight inits and investigations/007 CodeChallenge Identically random weights.mp4 65MB
  107. 10 - Metaparameters (activations, optimizers)/012 CodeChallenge Compare relu variants.mp4 64MB
  108. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/001 Leveraging sequences in deep learning.mp4 64MB
  109. 13 - Measuring model performance/002 Accuracy, precision, recall, F1.mp4 64MB
  110. 10 - Metaparameters (activations, optimizers)/018 SGD with momentum.mp4 62MB
  111. 10 - Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4 62MB
  112. 09 - Regularization/001 Regularization Concept and methods.mp4 62MB
  113. 25 - Ethics of deep learning/005 Accountability and making ethical AI.mp4 61MB
  114. 29 - Python intro Functions/003 Python libraries (pandas).mp4 61MB
  115. 29 - Python intro Functions/008 Classes and object-oriented programming.mp4 61MB
  116. 11 - FFNs (Feed-Forward Networks)/009 Scrambled MNIST.mp4 60MB
  117. 05 - Math, numpy, PyTorch/016 The t-test.mp4 60MB
  118. 15 - Weight inits and investigations/001 Explanation of weight matrix sizes.mp4 60MB
  119. 13 - Measuring model performance/006 CodeChallenge MNIST with unequal groups.mp4 59MB
  120. 31 - Python intro Text and plots/004 Making the graphs look nicer.mp4 59MB
  121. 05 - Math, numpy, PyTorch/011 Entropy and cross-entropy.mp4 59MB
  122. 30 - Python intro Flow control/004 Enumerate and zip.mp4 59MB
  123. 23 - Generative adversarial networks/003 CodeChallenge Linear GAN with FMNIST.mp4 59MB
  124. 25 - Ethics of deep learning/003 Some other possible ethical scenarios.mp4 58MB
  125. 11 - FFNs (Feed-Forward Networks)/010 Shifted MNIST.mp4 57MB
  126. 06 - Gradient descent/004 CodeChallenge unfortunate starting value.mp4 57MB
  127. 03 - Concepts in deep learning/005 Are artificial neurons like biological neurons.mp4 56MB
  128. 08 - Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4 56MB
  129. 01 - Introduction/001 How to learn from this course.mp4 55MB
  130. 08 - Overfitting and cross-validation/001 What is overfitting and is it as bad as they say.mp4 54MB
  131. 30 - Python intro Flow control/002 If-else statements, part 2.mp4 54MB
  132. 18 - Convolution and transformations/002 Feature maps and convolution kernels.mp4 54MB
  133. 11 - FFNs (Feed-Forward Networks)/011 CodeChallenge The mystery of the missing 7.mp4 53MB
  134. 14 - FFN milestone projects/006 Project 3 My solution.mp4 53MB
  135. 07 - ANNs (Artificial Neural Networks)/021 Reflection Are DL models understandable yet.mp4 52MB
  136. 09 - Regularization/011 The importance of equal batch sizes.mp4 51MB
  137. 23 - Generative adversarial networks/005 CodeChallenge Gaussians with fewer layers.mp4 51MB
  138. 18 - Convolution and transformations/008 Maxmean pooling.mp4 51MB
  139. 22 - Style transfer/005 CodeChallenge Style transfer with AlexNet.mp4 51MB
  140. 17 - Running models on a GPU/001 What is a GPU and why use it.mp4 50MB
  141. 09 - Regularization/006 Weight regularization (L1L2) math.mp4 49MB
  142. 18 - Convolution and transformations/010 To pool or to stride.mp4 49MB
  143. 05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding.mp4 49MB
  144. 08 - Overfitting and cross-validation/002 Cross-validation.mp4 49MB
  145. 31 - Python intro Text and plots/003 Subplot geometry.mp4 49MB
  146. 30 - Python intro Flow control/008 while loops.mp4 48MB
  147. 10 - Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4 48MB
  148. 31 - Python intro Text and plots/001 Printing and string interpolation.mp4 47MB
  149. 23 - Generative adversarial networks/006 CNN GAN with FMNIST.mp4 47MB
  150. 30 - Python intro Flow control/006 Initializing variables.mp4 46MB
  151. 27 - Python intro Data types/007 Booleans.mp4 46MB
  152. 05 - Math, numpy, PyTorch/012 Minmax and argminargmax.mp4 46MB
  153. 05 - Math, numpy, PyTorch/008 Matrix multiplication.mp4 45MB
  154. 10 - Metaparameters (activations, optimizers)/004 Data normalization.mp4 45MB
  155. 10 - Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4 45MB
  156. 30 - Python intro Flow control/003 For loops.mp4 45MB
  157. 18 - Convolution and transformations/009 Pooling in PyTorch.mp4 44MB
  158. 30 - Python intro Flow control/007 Single-line loops (list comprehension).mp4 44MB
  159. 05 - Math, numpy, PyTorch/003 Spectral theories in mathematics.mp4 44MB
  160. 23 - Generative adversarial networks/007 CodeChallenge CNN GAN with CIFAR.mp4 43MB
  161. 10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4 42MB
  162. 19 - Understand and design CNNs/003 CNN on shifted MNIST.mp4 41MB
  163. 05 - Math, numpy, PyTorch/014 Random sampling and sampling variability.mp4 41MB
  164. 27 - Python intro Data types/002 Variables.mp4 41MB
  165. 21 - Transfer learning/001 Transfer learning What, why, and when.mp4 40MB
  166. 29 - Python intro Functions/005 Creating functions.mp4 40MB
  167. 06 - Gradient descent/001 Overview of gradient descent.mp4 40MB
  168. 10 - Metaparameters (activations, optimizers)/022 CodeChallenge Adam with L2 regularization.mp4 40MB
  169. 10 - Metaparameters (activations, optimizers)/008 CodeChallenge Batch-normalize the qwerties.mp4 40MB
  170. 17 - Running models on a GPU/002 Implementation.mp4 40MB
  171. 29 - Python intro Functions/006 Global and local variable scopes.mp4 39MB
  172. 19 - Understand and design CNNs/014 CodeChallenge How low can you go.mp4 39MB
  173. 10 - Metaparameters (activations, optimizers)/006 Batch normalization.mp4 39MB
  174. 12 - More on data/009 Save and load trained models.mp4 39MB
  175. 23 - Generative adversarial networks/001 GAN What, why, and how.mp4 39MB
  176. 25 - Ethics of deep learning/002 Example case studies.mp4 38MB
  177. 13 - Measuring model performance/003 APRF in code.mp4 38MB
  178. 09 - Regularization/005 Dropout example 2.mp4 38MB
  179. 10 - Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4 38MB
  180. 31 - Python intro Text and plots/007 Export plots in low and high resolution.mp4 37MB
  181. 07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost).mp4 37MB
  182. 07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture.mp4 37MB
  183. 30 - Python intro Flow control/009 Broadcasting in numpy.mp4 37MB
  184. 17 - Running models on a GPU/003 CodeChallenge Run an experiment on the GPU.mp4 37MB
  185. 07 - ANNs (Artificial Neural Networks)/011 Linear solutions to linear problems.mp4 37MB
  186. 20 - CNN milestone projects/001 Project 1 Import and classify CIFAR10.mp4 37MB
  187. 10 - Metaparameters (activations, optimizers)/021 CodeChallenge Optimizers and... something.mp4 37MB
  188. 07 - ANNs (Artificial Neural Networks)/019 CodeChallenge convert sequential to class.mp4 37MB
  189. 27 - Python intro Data types/003 Math and printing.mp4 36MB
  190. 03 - Concepts in deep learning/002 How models learn.mp4 35MB
  191. 31 - Python intro Text and plots/005 Seaborn.mp4 34MB
  192. 25 - Ethics of deep learning/004 Will deep learning take our jobs.mp4 34MB
  193. 02 - Download all course materials/001 Downloading and using the code.mp4 34MB
  194. 11 - FFNs (Feed-Forward Networks)/008 CodeChallenge Optimizers and MNIST.mp4 33MB
  195. 05 - Math, numpy, PyTorch/013 Mean and variance.mp4 33MB
  196. 07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop).mp4 33MB
  197. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/002 How RNNs work.mp4 33MB
  198. 05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials.mp4 32MB
  199. 12 - More on data/008 Getting data into colab.mp4 32MB
  200. 30 - Python intro Flow control/001 If-else statements.mp4 30MB
  201. 07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs.mp4 30MB
  202. 03 - Concepts in deep learning/001 What is an artificial neural network.mp4 29MB
  203. 20 - CNN milestone projects/003 Project 2 CIFAR-autoencoder.mp4 29MB
  204. 28 - Python intro Indexing, slicing/002 Slicing.mp4 29MB
  205. 31 - Python intro Text and plots/002 Plotting dots and lines.mp4 29MB
  206. 11 - FFNs (Feed-Forward Networks)/004 CodeChallenge Binarized MNIST images.mp4 29MB
  207. 12 - More on data/011 Where to find online datasets.mp4 28MB
  208. 07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop).mp4 28MB
  209. 29 - Python intro Functions/002 Python libraries (numpy).mp4 28MB
  210. 06 - Gradient descent/006 CodeChallenge 2D gradient ascent.mp4 28MB
  211. 18 - Convolution and transformations/004 Convolution parameters (stride, padding).mp4 27MB
  212. 22 - Style transfer/003 The style transfer algorithm.mp4 27MB
  213. 08 - Overfitting and cross-validation/008 Cross-validation on regression.mp4 26MB
  214. 14 - FFN milestone projects/001 Project 1 A gratuitously complex adding machine.mp4 26MB
  215. 05 - Math, numpy, PyTorch/019 Derivatives product and chain rules.mp4 26MB
  216. 01 - Introduction/002 Using Udemy like a pro.mp4 26MB
  217. 06 - Gradient descent/002 What about local minima.mp4 26MB
  218. 10 - Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4 26MB
  219. 27 - Python intro Data types/004 Lists (1 of 2).mp4 25MB
  220. 29 - Python intro Functions/004 Getting help on functions.mp4 25MB
  221. 11 - FFNs (Feed-Forward Networks)/012 Universal approximation theorem.mp4 24MB
  222. 09 - Regularization/009 Training in mini-batches.mp4 24MB
  223. 25 - Ethics of deep learning/001 Will AI save us or destroy us.mp4 24MB
  224. 19 - Understand and design CNNs/001 The canonical CNN architecture.mp4 24MB
  225. 14 - FFN milestone projects/003 Project 2 Predicting heart disease.mp4 24MB
  226. 27 - Python intro Data types/005 Lists (2 of 2).mp4 24MB
  227. 28 - Python intro Indexing, slicing/001 Indexing.mp4 23MB
  228. 27 - Python intro Data types/008 Dictionaries.mp4 23MB
  229. 06 - Gradient descent/009 Vanishing and exploding gradients.mp4 22MB
  230. 21 - Transfer learning/004 Famous CNN architectures.mp4 22MB
  231. 16 - Autoencoders/001 What are autoencoders and what do they do.mp4 21MB
  232. 05 - Math, numpy, PyTorch/010 Logarithms.mp4 21MB
  233. 21 - Transfer learning/006 CodeChallenge VGG-16.mp4 20MB
  234. 05 - Math, numpy, PyTorch/007 OMG it's the dot product!.mp4 20MB
  235. 14 - FFN milestone projects/005 Project 3 FFN for missing data interpolation.mp4 20MB
  236. 20 - CNN milestone projects/004 Project 3 FMNIST.mp4 19MB
  237. 07 - ANNs (Artificial Neural Networks)/012 Why multilayer linear models don't exist.mp4 19MB
  238. 18 - Convolution and transformations/006 CodeChallenge Choose the parameters.mp4 19MB
  239. 13 - Measuring model performance/001 Two perspectives of the world.mp4 19MB
  240. 12 - More on data/004 What to do about unbalanced designs.mp4 19MB
  241. 05 - Math, numpy, PyTorch/018 Derivatives find minima.mp4 19MB
  242. 13 - Measuring model performance/008 Better performance in test than train.mp4 18MB
  243. 05 - Math, numpy, PyTorch/006 Vector and matrix transpose.mp4 18MB
  244. 26 - Where to go from here/001 How to learn topic _X_ in deep learning.mp4 17MB
  245. 22 - Style transfer/001 What is style transfer and how does it work.mp4 17MB
  246. 05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers.mp4 16MB
  247. 09 - Regularization/002 train() and eval() modes.mp4 16MB
  248. 27 - Python intro Data types/006 Tuples.mp4 15MB
  249. 06 - Gradient descent/010 Tangent Notebook revision history.mp4 15MB
  250. 30 - Python intro Flow control/005 Continue.mp4 14MB
  251. 29 - Python intro Functions/001 Inputs and outputs.mp4 13MB
  252. 05 - Math, numpy, PyTorch/005 Converting reality to numbers.mp4 13MB
  253. 08 - Overfitting and cross-validation/003 Generalization.mp4 13MB
  254. 11 - FFNs (Feed-Forward Networks)/001 What are fully-connected and feedforward networks.mp4 13MB
  255. 10 - Metaparameters (activations, optimizers)/001 What are metaparameters.mp4 12MB
  256. 27 - Python intro Data types/001 How to learn from the Python tutorial.mp4 12MB
  257. 15 - Weight inits and investigations/010 Use default inits or apply your own.mp4 11MB
  258. 29 - Python intro Functions/007 Copies and referents of variables.mp4 11MB
  259. 04 - About the Python tutorial/001 Should you watch the Python tutorial.mp4 9MB
  260. 19 - Understand and design CNNs/016 So many possibilities! How to create a CNN.mp4 9MB
  261. 05 - Math, numpy, PyTorch/002 Introduction to this section.mp4 4MB
  262. 02 - Download all course materials/002 My policy on code-sharing.mp4 4MB
  263. 02 - Download all course materials/001 DUDL-PythonCode.zip 660KB
  264. 19 - Understand and design CNNs/005 Examine feature map activations_en.srt 39KB
  265. 07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset)_en.srt 39KB
  266. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation_en.srt 38KB
  267. 19 - Understand and design CNNs/002 CNN to classify MNIST digits_en.srt 37KB
  268. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum_en.srt 36KB
  269. 07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison_en.srt 35KB
  270. 19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition)_en.srt 35KB
  271. 07 - ANNs (Artificial Neural Networks)/006 ANN for regression_en.srt 35KB
  272. 16 - Autoencoders/006 Autoencoder with tied weights_en.srt 34KB
  273. 07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties_en.srt 33KB
  274. 19 - Understand and design CNNs/004 Classify Gaussian blurs_en.srt 33KB
  275. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM_en.srt 32KB
  276. 09 - Regularization/004 Dropout regularization in practice_en.srt 32KB
  277. 11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits_en.srt 32KB
  278. 15 - Weight inits and investigations/009 Learning-related changes in weights_en.srt 32KB
  279. 18 - Convolution and transformations/001 Convolution concepts_en.srt 31KB
  280. 22 - Style transfer/004 Transferring the screaming bathtub_en.srt 31KB
  281. 23 - Generative adversarial networks/002 Linear GAN with MNIST_en.srt 31KB
  282. 16 - Autoencoders/005 The latent code of MNIST_en.srt 30KB
  283. 09 - Regularization/003 Dropout regularization_en.srt 30KB
  284. 07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth_en.srt 30KB
  285. 29 - Python intro Functions/005 Creating functions_en.srt 30KB
  286. 18 - Convolution and transformations/003 Convolution in code_en.srt 29KB
  287. 08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn_en.srt 29KB
  288. 19 - Understand and design CNNs/010 CodeChallenge Custom loss functions_en.srt 29KB
  289. 07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN_en.srt 28KB
  290. 12 - More on data/003 CodeChallenge unbalanced data_en.srt 28KB
  291. 16 - Autoencoders/003 CodeChallenge How many units_en.srt 28KB
  292. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences_en.srt 28KB
  293. 21 - Transfer learning/007 Pretraining with autoencoders_en.srt 28KB
  294. 08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader_en.srt 28KB
  295. 12 - More on data/007 Data feature augmentation_en.srt 27KB
  296. 07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes_en.srt 27KB
  297. 07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture_en.srt 27KB
  298. 30 - Python intro Flow control/008 while loops_en.srt 27KB
  299. 05 - Math, numpy, PyTorch/009 Softmax_en.srt 27KB
  300. 14 - FFN milestone projects/004 Project 2 My solution_en.srt 27KB
  301. 27 - Python intro Data types/007 Booleans_en.srt 27KB
  302. 10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum)_en.srt 26KB
  303. 27 - Python intro Data types/002 Variables_en.srt 26KB
  304. 06 - Gradient descent/007 Parametric experiments on g.d_en.srt 26KB
  305. 09 - Regularization/006 Weight regularization (L1L2) math_en.srt 26KB
  306. 31 - Python intro Text and plots/004 Making the graphs look nicer_en.srt 26KB
  307. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch_en.srt 26KB
  308. 10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch_en.srt 26KB
  309. 27 - Python intro Data types/003 Math and printing_en.srt 26KB
  310. 18 - Convolution and transformations/008 Maxmean pooling_en.srt 26KB
  311. 29 - Python intro Functions/008 Classes and object-oriented programming_en.srt 26KB
  312. 10 - Metaparameters (activations, optimizers)/009 Activation functions_en.srt 26KB
  313. 18 - Convolution and transformations/012 Creating and using custom DataLoaders_en.srt 25KB
  314. 12 - More on data/001 Anatomy of a torch dataset and dataloader_en.srt 25KB
  315. 21 - Transfer learning/008 CIFAR10 with autoencoder-pretrained model_en.srt 25KB
  316. 10 - Metaparameters (activations, optimizers)/002 The wine quality dataset_en.srt 25KB
  317. 07 - ANNs (Artificial Neural Networks)/016 Depth vs. breadth number of parameters_en.srt 25KB
  318. 31 - Python intro Text and plots/006 Images_en.srt 25KB
  319. 30 - Python intro Flow control/006 Initializing variables_en.srt 25KB
  320. 16 - Autoencoders/004 AEs for occlusion_en.srt 24KB
  321. 26 - Where to go from here/002 How to read academic DL papers_en.srt 24KB
  322. 05 - Math, numpy, PyTorch/011 Entropy and cross-entropy_en.srt 24KB
  323. 30 - Python intro Flow control/010 Function error checking and handling_en.srt 24KB
  324. 30 - Python intro Flow control/003 For loops_en.srt 24KB
  325. 19 - Understand and design CNNs/006 CodeChallenge Softcode internal parameters_en.srt 24KB
  326. 10 - Metaparameters (activations, optimizers)/013 CodeChallenge Predict sugar_en.srt 24KB
  327. 08 - Overfitting and cross-validation/002 Cross-validation_en.srt 24KB
  328. 21 - Transfer learning/001 Transfer learning What, why, and when_en.srt 24KB
  329. 06 - Gradient descent/003 Gradient descent in 1D_en.srt 24KB
  330. 15 - Weight inits and investigations/006 CodeChallenge Xavier vs. Kaiming_en.srt 24KB
  331. 21 - Transfer learning/005 Transfer learning with ResNet-18_en.srt 24KB
  332. 11 - FFNs (Feed-Forward Networks)/005 CodeChallenge Data normalization_en.srt 24KB
  333. 19 - Understand and design CNNs/008 Do autoencoders clean Gaussians_en.srt 23KB
  334. 05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials_en.srt 23KB
  335. 10 - Metaparameters (activations, optimizers)/014 Loss functions_en.srt 23KB
  336. 31 - Python intro Text and plots/001 Printing and string interpolation_en.srt 23KB
  337. 03 - Concepts in deep learning/005 Are artificial neurons like biological neurons_en.srt 23KB
  338. 12 - More on data/005 Data oversampling in MNIST_en.srt 23KB
  339. 15 - Weight inits and investigations/002 A surprising demo of weight initializations_en.srt 23KB
  340. 18 - Convolution and transformations/011 Image transforms_en.srt 23KB
  341. 23 - Generative adversarial networks/001 GAN What, why, and how_en.srt 23KB
  342. 06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate_en.srt 23KB
  343. 12 - More on data/002 Data size and network size_en.srt 23KB
  344. 03 - Concepts in deep learning/003 The role of DL in science and knowledge_en.srt 22KB
  345. 19 - Understand and design CNNs/011 Discover the Gaussian parameters_en.srt 22KB
  346. 31 - Python intro Text and plots/003 Subplot geometry_en.srt 22KB
  347. 10 - Metaparameters (activations, optimizers)/003 CodeChallenge Minibatch size in the wine dataset_en.srt 22KB
  348. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/006 More on RNNs Hidden states, embeddings_en.srt 22KB
  349. 30 - Python intro Flow control/002 If-else statements, part 2_en.srt 22KB
  350. 16 - Autoencoders/002 Denoising MNIST_en.srt 22KB
  351. 05 - Math, numpy, PyTorch/013 Mean and variance_en.srt 22KB
  352. 15 - Weight inits and investigations/005 Xavier and Kaiming initializations_en.srt 22KB
  353. 17 - Running models on a GPU/001 What is a GPU and why use it_en.srt 22KB
  354. 07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop)_en.srt 21KB
  355. 23 - Generative adversarial networks/004 CNN GAN with Gaussians_en.srt 21KB
  356. 10 - Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam)_en.srt 21KB
  357. 11 - FFNs (Feed-Forward Networks)/006 Distributions of weights pre- and post-learning_en.srt 21KB
  358. 12 - More on data/010 Save the best-performing model_en.srt 21KB
  359. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/002 How RNNs work_en.srt 21KB
  360. 30 - Python intro Flow control/007 Single-line loops (list comprehension)_en.srt 21KB
  361. 30 - Python intro Flow control/001 If-else statements_en.srt 21KB
  362. 21 - Transfer learning/003 CodeChallenge letters to numbers_en.srt 21KB
  363. 06 - Gradient descent/005 Gradient descent in 2D_en.srt 21KB
  364. 03 - Concepts in deep learning/001 What is an artificial neural network_en.srt 21KB
  365. 30 - Python intro Flow control/009 Broadcasting in numpy_en.srt 21KB
  366. 06 - Gradient descent/001 Overview of gradient descent_en.srt 20KB
  367. 05 - Math, numpy, PyTorch/008 Matrix multiplication_en.srt 20KB
  368. 27 - Python intro Data types/004 Lists (1 of 2)_en.srt 20KB
  369. 10 - Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs_en.srt 20KB
  370. 29 - Python intro Functions/003 Python libraries (pandas)_en.srt 20KB
  371. 18 - Convolution and transformations/009 Pooling in PyTorch_en.srt 19KB
  372. 29 - Python intro Functions/002 Python libraries (numpy)_en.srt 19KB
  373. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/008 The LSTM and GRU classes_en.srt 19KB
  374. 18 - Convolution and transformations/007 Transpose convolution_en.srt 19KB
  375. 10 - Metaparameters (activations, optimizers)/004 Data normalization_en.srt 19KB
  376. 19 - Understand and design CNNs/015 CodeChallenge Varying number of channels_en.srt 19KB
  377. 29 - Python intro Functions/006 Global and local variable scopes_en.srt 19KB
  378. 07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs_en.srt 19KB
  379. 05 - Math, numpy, PyTorch/016 The t-test_en.srt 19KB
  380. 15 - Weight inits and investigations/008 Freezing weights during learning_en.srt 19KB
  381. 03 - Concepts in deep learning/004 Running experiments to understand DL_en.srt 19KB
  382. 13 - Measuring model performance/004 APRF example 1 wine quality_en.srt 19KB
  383. 07 - ANNs (Artificial Neural Networks)/017 Defining models using sequential vs. class_en.srt 18KB
  384. 09 - Regularization/001 Regularization Concept and methods_en.srt 18KB
  385. 09 - Regularization/007 L2 regularization in practice_en.srt 18KB
  386. 18 - Convolution and transformations/005 The Conv2 class in PyTorch_en.srt 18KB
  387. 24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/001 Leveraging sequences in deep learning_en.srt 18KB
  388. 03 - Concepts in deep learning/002 How models learn_en.srt 18KB
  389. 10 - Metaparameters (activations, optimizers)/006 Batch normalization_en.srt 18KB
  390. 12 - More on data/006 Data noise augmentation (with devset+test)_en.srt 18KB
  391. 08 - Overfitting and cross-validation/004 Cross-validation -- manual separation_en.srt 18KB
  392. 15 - Weight inits and investigations/004 CodeChallenge Weight variance inits_en.srt 18KB
  393. 11 - FFNs (Feed-Forward Networks)/002 The MNIST dataset_en.srt 18KB
  394. 08 - Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_en.srt 18KB
  395. 15 - Weight inits and investigations/003 Theory Why and how to initialize weights_en.srt 18KB
  396. 05 - Math, numpy, PyTorch/012 Minmax and argminargmax_en.srt 17KB
  397. 09 - Regularization/012 CodeChallenge Effects of mini-batch size_en.srt 17KB
  398. 18 - Convolution and transformations/004 Convolution parameters (stride, padding)_en.srt 17KB
  399. 28 - Python intro Indexing, slicing/001 Indexing_en.srt 17KB
  400. 13 - Measuring model performance/002 Accuracy, precision, recall, F1_en.srt 17KB
  401. 15 - Weight inits and investigations/007 CodeChallenge Identically random weights_en.srt 17KB
  402. 28 - Python intro Indexing, slicing/002 Slicing_en.srt 17KB
  403. 10 - Metaparameters (activations, optimizers)/023 Learning rate decay_en.srt 17KB
  404. 07 - ANNs (Artificial Neural Networks)/014 CodeChallenge more qwerties!_en.srt 17KB
  405. 11 - FFNs (Feed-Forward Networks)/007 CodeChallenge MNIST and breadth vs. depth_en.srt 17KB
  406. 31 - Python intro Text and plots/002 Plotting dots and lines_en.srt 17KB
  407. 09 - Regularization/008 L1 regularization in practice_en.srt 17KB
  408. 20 - CNN milestone projects/002 Project 1 My solution_en.srt 17KB
  409. 15 - Weight inits and investigations/001 Explanation of weight matrix sizes_en.srt 17KB
  410. 06 - Gradient descent/002 What about local minima_en.srt 17KB
  411. 13 - Measuring model performance/005 APRF example 2 MNIST_en.srt 17KB
  412. 27 - Python intro Data types/008 Dictionaries_en.srt 16KB
  413. 10 - Metaparameters (activations, optimizers)/010 Activation functions in PyTorch_en.srt 16KB
  414. 19 - Understand and design CNNs/007 CodeChallenge How wide the FC_en.srt 16KB
  415. 16 - Autoencoders/001 What are autoencoders and what do they do_en.srt 16KB
  416. 14 - FFN milestone projects/002 Project 1 My solution_en.srt 16KB
  417. 20 - CNN milestone projects/005 Project 4 Psychometric functions in CNNs_en.srt 16KB
  418. 09 - Regularization/009 Training in mini-batches_en.srt 16KB
  419. 22 - Style transfer/002 The Gram matrix (feature activation covariance)_en.srt 16KB
  420. 25 - Ethics of deep learning/005 Accountability and making ethical AI_en.srt 16KB
  421. 10 - Metaparameters (activations, optimizers)/024 How to pick the right metaparameters_en.srt 16KB
  422. 11 - FFNs (Feed-Forward Networks)/010 Shifted MNIST_en.srt 16KB
  423. 05 - Math, numpy, PyTorch/014 Random sampling and sampling variability_en.srt 16KB
  424. 30 - Python intro Flow control/004 Enumerate and zip_en.srt 15KB
  425. 06 - Gradient descent/004 CodeChallenge unfortunate starting value_en.srt 15KB
  426. 31 - Python intro Text and plots/005 Seaborn_en.srt 15KB
  427. 11 - FFNs (Feed-Forward Networks)/011 CodeChallenge The mystery of the missing 7_en.srt 15KB
  428. 19 - Understand and design CNNs/001 The canonical CNN architecture_en.srt 15KB
  429. 09 - Regularization/010 Batch training in action_en.srt 15KB
  430. 07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop)_en.srt 15KB
  431. 25 - Ethics of deep learning/003 Some other possible ethical scenarios_en.srt 15KB
  432. 22 - Style transfer/003 The style transfer algorithm_en.srt 15KB
  433. 25 - Ethics of deep learning/004 Will deep learning take our jobs_en.srt 14KB
  434. 17 - Running models on a GPU/002 Implementation_en.srt 14KB
  435. 10 - Metaparameters (activations, optimizers)/020 Optimizers comparison_en.srt 14KB
  436. 07 - ANNs (Artificial Neural Networks)/015 Comparing the number of hidden units_en.srt 14KB
  437. 21 - Transfer learning/002 Transfer learning MNIST - FMNIST_en.srt 14KB
  438. 18 - Convolution and transformations/010 To pool or to stride_en.srt 14KB
  439. 27 - Python intro Data types/005 Lists (2 of 2)_en.srt 14KB
  440. 14 - FFN milestone projects/005 Project 3 FFN for missing data interpolation_en.srt 14KB
  441. 25 - Ethics of deep learning/001 Will AI save us or destroy us_en.srt 14KB
  442. 13 - Measuring model performance/007 Computation time_en.srt 14KB
  443. 19 - Understand and design CNNs/013 Dropout in CNNs_en.srt 14KB
  444. 19 - Understand and design CNNs/009 CodeChallenge AEs and occluded Gaussians_en.srt 13KB
  445. 18 - Convolution and transformations/002 Feature maps and convolution kernels_en.srt 13KB
  446. 05 - Math, numpy, PyTorch/007 OMG it's the dot product!_en.srt 13KB
  447. 07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost)_en.srt 13KB
  448. 23 - Generative adversarial networks/003 CodeChallenge Linear GAN with FMNIST_en.srt 13KB
  449. 08 - Overfitting and cross-validation/007 Splitting data into train, devset, test_en.srt 13KB
  450. 10 - Metaparameters (activations, optimizers)/005 The importance of data normalization_en.srt 13KB
  451. 05 - Math, numpy, PyTorch/003 Spectral theories in mathematics_en.srt 13KB
  452. 10 - Metaparameters (activations, optimizers)/011 Activation functions comparison_en.srt 13KB
  453. 05 - Math, numpy, PyTorch/019 Derivatives product and chain rules_en.srt 13KB
  454. 01 - Introduction/001 How to learn from this course_en.srt 12KB
  455. 13 - Measuring model performance/006 CodeChallenge MNIST with unequal groups_en.srt 12KB
  456. 07 - ANNs (Artificial Neural Networks)/021 Reflection Are DL models understandable yet_en.srt 12KB
  457. 26 - Where to go from here/001 How to learn topic _X_ in deep learning_en.srt 12KB
  458. 01 - Introduction/002 Using Udemy like a pro_en.srt 12KB
  459. 07 - ANNs (Artificial Neural Networks)/011 Linear solutions to linear problems_en.srt 12KB
  460. 05 - Math, numpy, PyTorch/018 Derivatives find minima_en.srt 12KB
  461. 19 - Understand and design CNNs/003 CNN on shifted MNIST_en.srt 12KB
  462. 27 - Python intro Data types/006 Tuples_en.srt 12KB
  463. 13 - Measuring model performance/008 Better performance in test than train_en.srt 12KB
  464. 08 - Overfitting and cross-validation/008 Cross-validation on regression_en.srt 12KB
  465. 14 - FFN milestone projects/006 Project 3 My solution_en.srt 11KB
  466. 05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding_en.srt 11KB
  467. 11 - FFNs (Feed-Forward Networks)/012 Universal approximation theorem_en.srt 11KB
  468. 23 - Generative adversarial networks/007 CodeChallenge CNN GAN with CIFAR_en.srt 11KB
  469. 10 - Metaparameters (activations, optimizers)/018 SGD with momentum_en.srt 11KB
  470. 05 - Math, numpy, PyTorch/010 Logarithms_en.srt 11KB
  471. 31 - Python intro Text and plots/007 Export plots in low and high resolution_en.srt 11KB
  472. 10 - Metaparameters (activations, optimizers)/012 CodeChallenge Compare relu variants_en.srt 11KB
  473. 11 - FFNs (Feed-Forward Networks)/009 Scrambled MNIST_en.srt 11KB
  474. 12 - More on data/004 What to do about unbalanced designs_en.srt 11KB
  475. 29 - Python intro Functions/004 Getting help on functions_en.srt 11KB
  476. 10 - Metaparameters (activations, optimizers)/007 Batch normalization in practice_en.srt 11KB
  477. 14 - FFN milestone projects/003 Project 2 Predicting heart disease_en.srt 11KB
  478. 14 - FFN milestone projects/001 Project 1 A gratuitously complex adding machine_en.srt 10KB
  479. 05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers_en.srt 10KB
  480. 20 - CNN milestone projects/001 Project 1 Import and classify CIFAR10_en.srt 10KB
  481. 29 - Python intro Functions/001 Inputs and outputs_en.srt 10KB
  482. 22 - Style transfer/005 CodeChallenge Style transfer with AlexNet_en.srt 10KB
  483. 10 - Metaparameters (activations, optimizers)/022 CodeChallenge Adam with L2 regularization_en.srt 10KB
  484. 13 - Measuring model performance/001 Two perspectives of the world_en.srt 10KB
  485. 09 - Regularization/002 train() and eval() modes_en.srt 10KB
  486. 18 - Convolution and transformations/006 CodeChallenge Choose the parameters_en.srt 10KB
  487. 30 - Python intro Flow control/005 Continue_en.srt 10KB
  488. 05 - Math, numpy, PyTorch/006 Vector and matrix transpose_en.srt 10KB
  489. 19 - Understand and design CNNs/014 CodeChallenge How low can you go_en.srt 10KB
  490. 11 - FFNs (Feed-Forward Networks)/008 CodeChallenge Optimizers and MNIST_en.srt 10KB
  491. 17 - Running models on a GPU/003 CodeChallenge Run an experiment on the GPU_en.srt 9KB
  492. 07 - ANNs (Artificial Neural Networks)/019 CodeChallenge convert sequential to class_en.srt 9KB
  493. 05 - Math, numpy, PyTorch/005 Converting reality to numbers_en.srt 9KB
  494. 09 - Regularization/011 The importance of equal batch sizes_en.srt 9KB
  495. 02 - Download all course materials/001 Downloading and using the code_en.srt 9KB
  496. 13 - Measuring model performance/003 APRF in code_en.srt 9KB
  497. 10 - Metaparameters (activations, optimizers)/021 CodeChallenge Optimizers and... something_en.srt 9KB
  498. 23 - Generative adversarial networks/006 CNN GAN with FMNIST_en.srt 9KB
  499. 07 - ANNs (Artificial Neural Networks)/012 Why multilayer linear models don't exist_en.srt 9KB
  500. 09 - Regularization/005 Dropout example 2_en.srt 9KB
  501. 25 - Ethics of deep learning/002 Example case studies_en.srt 9KB
  502. 06 - Gradient descent/009 Vanishing and exploding gradients_en.srt 9KB
  503. 12 - More on data/009 Save and load trained models_en.srt 9KB
  504. 23 - Generative adversarial networks/005 CodeChallenge Gaussians with fewer layers_en.srt 9KB
  505. 12 - More on data/008 Getting data into colab_en.srt 9KB
  506. 08 - Overfitting and cross-validation/003 Generalization_en.srt 9KB
  507. 21 - Transfer learning/004 Famous CNN architectures_en.srt 8KB
  508. 12 - More on data/011 Where to find online datasets_en.srt 8KB
  509. 06 - Gradient descent/006 CodeChallenge 2D gradient ascent_en.srt 7KB
  510. 10 - Metaparameters (activations, optimizers)/008 CodeChallenge Batch-normalize the qwerties_en.srt 7KB
  511. 11 - FFNs (Feed-Forward Networks)/004 CodeChallenge Binarized MNIST images_en.srt 7KB
  512. 10 - Metaparameters (activations, optimizers)/001 What are metaparameters_en.srt 7KB
  513. 29 - Python intro Functions/007 Copies and referents of variables_en.srt 7KB
  514. 20 - CNN milestone projects/003 Project 2 CIFAR-autoencoder_en.srt 7KB
  515. 11 - FFNs (Feed-Forward Networks)/001 What are fully-connected and feedforward networks_en.srt 7KB
  516. 19 - Understand and design CNNs/016 So many possibilities! How to create a CNN_en.srt 6KB
  517. 15 - Weight inits and investigations/010 Use default inits or apply your own_en.srt 6KB
  518. 22 - Style transfer/001 What is style transfer and how does it work_en.srt 6KB
  519. 04 - About the Python tutorial/001 Should you watch the Python tutorial_en.srt 6KB
  520. 20 - CNN milestone projects/004 Project 3 FMNIST_en.srt 5KB
  521. 21 - Transfer learning/006 CodeChallenge VGG-16_en.srt 5KB
  522. 27 - Python intro Data types/001 How to learn from the Python tutorial_en.srt 5KB
  523. 32 - Bonus section/001 Bonus content.html 4KB
  524. 05 - Math, numpy, PyTorch/002 Introduction to this section_en.srt 3KB
  525. 06 - Gradient descent/010 Tangent Notebook revision history_en.srt 3KB
  526. 02 - Download all course materials/002 My policy on code-sharing_en.srt 2KB
  527. 05 - Math, numpy, PyTorch/001 PyTorch or TensorFlow.html 1KB
  528. 07 - ANNs (Artificial Neural Networks)/020 Diversity of ANN visual representations.html 517B
  529. 0. Websites you may like/[CourseClub.Me].url 122B
  530. 06 - Gradient descent/[CourseClub.Me].url 122B
  531. 19 - Understand and design CNNs/[CourseClub.Me].url 122B
  532. 30 - Python intro Flow control/[CourseClub.Me].url 122B
  533. [CourseClub.Me].url 122B
  534. 0. Websites you may like/[GigaCourse.Com].url 49B
  535. 06 - Gradient descent/[GigaCourse.Com].url 49B
  536. 19 - Understand and design CNNs/[GigaCourse.Com].url 49B
  537. 30 - Python intro Flow control/[GigaCourse.Com].url 49B
  538. [GigaCourse.Com].url 49B