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[] Udemy - The Complete Neural Networks Bootcamp Theory, Applications

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文件列表

  1. 30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder.mp4 404MB
  2. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network.mp4 333MB
  3. 21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE.mp4 319MB
  4. 30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model.mp4 260MB
  5. 21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap.mp4 259MB
  6. 14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN.mp4 251MB
  7. 18. Transfer Learning in PyTorch - Image Classification/1. Data Augmentation.mp4 225MB
  8. 8. Introduction to PyTorch/9. Loss Functions in PyTorch.mp4 223MB
  9. 33. Build a Chatbot with Transformers/16. Loss with Label Smoothing.mp4 215MB
  10. 27. Practical Recurrent Networks in PyTorch/6. Generating Text.mp4 178MB
  11. 18. Transfer Learning in PyTorch - Image Classification/2. Loading the Dataset.mp4 177MB
  12. 30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2.mp4 176MB
  13. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network.mp4 171MB
  14. 34. Universal Transformers/2. Practical Universal Transformers Modifying the Transformers code.mp4 161MB
  15. 31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function.mp4 159MB
  16. 1. How Neural Networks and Backpropagation Works/1. What Can Deep Learning Do.mp4 156MB
  17. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network.mp4 156MB
  18. 27. Practical Recurrent Networks in PyTorch/5. Training the Network.mp4 152MB
  19. 15. CNN Architectures/3. Residual Networks Part 2.mp4 151MB
  20. 11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation.mp4 148MB
  21. 8. Introduction to PyTorch/4. How PyTorch Works.mp4 147MB
  22. 16. Practical Residual Networks in PyTorch/4. Practical ResNet Part 4.mp4 143MB
  23. 19. Convolutional Networks Visualization/2. Processing the Model.mp4 142MB
  24. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/2. Importing and Defining Parameters.mp4 142MB
  25. 30. Practical Sequence Modelling in PyTorch Chatbot Application/6. Designing the Decoder Part 1.mp4 139MB
  26. 36. BERT/5. Exploring Transformers.mp4 137MB
  27. 31. Practical Sequence Modelling in PyTorch Image Captioning/4. Constructing the Dataset Part 1.mp4 136MB
  28. 33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2.mp4 135MB
  29. 20. YOLO Object Detection (Theory)/1. YOLO Theory Part 1.mp4 134MB
  30. 19. Convolutional Networks Visualization/3. Visualizing the Feature Maps.mp4 133MB
  31. 14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN.mp4 131MB
  32. 31. Practical Sequence Modelling in PyTorch Image Captioning/9. Creating the Decoder Part 3.mp4 131MB
  33. 24. Practical Neural Style Transfer in PyTorch/4. NST Practical Part 4.mp4 131MB
  34. 28. Saving and Loading Models/1. Saving and Loading Part 1.mp4 131MB
  35. 24. Practical Neural Style Transfer in PyTorch/2. NST Practical Part 2.mp4 128MB
  36. 2. Loss Functions/10. Triplet Ranking Loss.mp4 126MB
  37. 20. YOLO Object Detection (Theory)/3. YOLO Theory Part 3.mp4 124MB
  38. 20. YOLO Object Detection (Theory)/6. YOLO Theory Part 6.mp4 124MB
  39. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/1. Part 1 Data Preprocessing.mp4 124MB
  40. 33. Build a Chatbot with Transformers/10. MultiHead Attention Implementation Part 3.mp4 123MB
  41. 15. CNN Architectures/2. Residual Networks Part 1.mp4 122MB
  42. 18. Transfer Learning in PyTorch - Image Classification/6. Testing and Visualizing the results.mp4 118MB
  43. 31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1.mp4 118MB
  44. 33. Build a Chatbot with Transformers/14. Transformer.mp4 117MB
  45. 34. Universal Transformers/3. Transformers for other tasks.mp4 113MB
  46. 27. Practical Recurrent Networks in PyTorch/4. Creating the Network.mp4 112MB
  47. 25. Recurrent Neural Networks/7. LSTMs.mp4 112MB
  48. 1. How Neural Networks and Backpropagation Works/4. The Perceptron.mp4 111MB
  49. 33. Build a Chatbot with Transformers/19. Evaluation Function.mp4 110MB
  50. 7. Weight Initialization/3. Xavier Initialization.mp4 110MB
  51. 27. Practical Recurrent Networks in PyTorch/2. Processing the Text.mp4 109MB
  52. 37. Vision Transformers/3. Vision Transformer Part 3.mp4 106MB
  53. 24. Practical Neural Style Transfer in PyTorch/3. NST Practical Part 3.mp4 106MB
  54. 20. YOLO Object Detection (Theory)/5. YOLO Theory Part 5.mp4 105MB
  55. 31. Practical Sequence Modelling in PyTorch Image Captioning/11. Defining Hyperparameters.mp4 105MB
  56. 22. Practical Variational Autoencoders in PyTorch/2. Practical VAE Part 2.mp4 104MB
  57. 16. Practical Residual Networks in PyTorch/3. Practical ResNet Part 3.mp4 103MB
  58. 18. Transfer Learning in PyTorch - Image Classification/4. Understanding the data.mp4 102MB
  59. 22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1.mp4 101MB
  60. 33. Build a Chatbot with Transformers/18. Training Function.mp4 101MB
  61. 4. Regularization and Normalization/6. Batch Normalization.mp4 100MB
  62. 13. Convolutional Neural Networks/13. DropBlock Dropout in CNNs.mp4 100MB
  63. 8. Introduction to PyTorch/3. Installing PyTorch and an Introduction.mp4 99MB
  64. 11. Implementing a Neural Network from Scratch with Numpy/6. Backpropagation Equations.mp4 99MB
  65. 31. Practical Sequence Modelling in PyTorch Image Captioning/8. Creating the Decoder Part 2.mp4 97MB
  66. 18. Transfer Learning in PyTorch - Image Classification/3. Modifying the Network.mp4 97MB
  67. 28. Saving and Loading Models/2. Saving and Loading Part 2.mp4 97MB
  68. 32. Transformers/3. Positional Encoding.mp4 96MB
  69. 15. CNN Architectures/5. Densely Connected Networks.mp4 95MB
  70. 33. Build a Chatbot with Transformers/20. Main Function and User Evaluation.mp4 93MB
  71. 22. Practical Variational Autoencoders in PyTorch/3. Practical VAE Part 3.mp4 93MB
  72. 30. Practical Sequence Modelling in PyTorch Chatbot Application/2. Understanding the Encoder.mp4 93MB
  73. 33. Build a Chatbot with Transformers/5. Dataset Preprocessing Part 5.mp4 92MB
  74. 31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function.mp4 91MB
  75. 38. GPT/1. GPT Part 1.mp4 89MB
  76. 8. Introduction to PyTorch/5. Torch Tensors - Part 1.mp4 87MB
  77. 33. Build a Chatbot with Transformers/12. Encoder Layer.mp4 87MB
  78. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/3. Defining the Network Class.mp4 86MB
  79. 16. Practical Residual Networks in PyTorch/2. Practical ResNet Part 2.mp4 86MB
  80. 5. Optimization/13. AMSGrad.mp4 86MB
  81. 37. Vision Transformers/1. Vision Transformer Part 1.mp4 85MB
  82. 21. Autoencoders and Variational Autoencoders/7. Deep Fake.mp4 85MB
  83. 11. Implementing a Neural Network from Scratch with Numpy/3. Forward Propagation.mp4 85MB
  84. 31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder.mp4 85MB
  85. 33. Build a Chatbot with Transformers/1. Dataset Preprocessing Part 1.mp4 83MB
  86. 29. Sequence Modelling/1. Sequence Modeling.mp4 82MB
  87. 33. Build a Chatbot with Transformers/7. Embeddings.mp4 81MB
  88. 13. Convolutional Neural Networks/8. Activation, Pooling and FC.mp4 81MB
  89. 20. YOLO Object Detection (Theory)/2. YOLO Theory Part 2.mp4 81MB
  90. 33. Build a Chatbot with Transformers/3. Dataset Preprocessing Part 3.mp4 80MB
  91. 5. Optimization/9. Adam Optimization.mp4 78MB
  92. 2. Loss Functions/2. L1 Loss (MAE).mp4 77MB
  93. 20. YOLO Object Detection (Theory)/8. YOLO Theory Part 8.mp4 77MB
  94. 8. Introduction to PyTorch/8. Automatic Differentiation.mp4 76MB
  95. 33. Build a Chatbot with Transformers/6. Data Loading and Masking.mp4 76MB
  96. 5. Optimization/11. Weight Decay.mp4 76MB
  97. 33. Build a Chatbot with Transformers/15. AdamWarmup.mp4 75MB
  98. 32. Transformers/15. Dropout.mp4 75MB
  99. 4. Regularization and Normalization/3. Dropout.mp4 75MB
  100. 8. Introduction to PyTorch/7. Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp4 75MB
  101. 30. Practical Sequence Modelling in PyTorch Chatbot Application/1. Introduction.mp4 74MB
  102. 19. Convolutional Networks Visualization/1. Data and the Model.mp4 74MB
  103. 31. Practical Sequence Modelling in PyTorch Image Captioning/3. Accuracy Calculation.mp4 74MB
  104. 26. Word Embeddings/1. What are Word Embeddings.mp4 73MB
  105. 10. Visualize the Learning Process/5. Visualize Learning Part 5.mp4 72MB
  106. 16. Practical Residual Networks in PyTorch/1. Practical ResNet Part 1.mp4 72MB
  107. 17. Transposed Convolutions/2. Convolution Operation as Matrix Multiplication.mp4 71MB
  108. 11. Implementing a Neural Network from Scratch with Numpy/1. The Dataset and Hyperparameters.mp4 71MB
  109. 21. Autoencoders and Variational Autoencoders/4. Variational Autoencoders.mp4 70MB
  110. 20. YOLO Object Detection (Theory)/7. YOLO Theory Part 7.mp4 70MB
  111. 27. Practical Recurrent Networks in PyTorch/3. Defining and Visualizing the Parameters.mp4 70MB
  112. 6. Hyperparameter Tuning and Learning Rate Scheduling/3. Cyclic Learning Rate.mp4 69MB
  113. 23. Neural Style Transfer/3. NST Theory Part 3.mp4 69MB
  114. 11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function.mp4 68MB
  115. 8. Introduction to PyTorch/6. Torch Tensors - Part 2.mp4 68MB
  116. 2. Loss Functions/9. Hinge Loss.mp4 67MB
  117. 25. Recurrent Neural Networks/6. Vanishing and Exploding Gradient Problem.mp4 67MB
  118. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/3. Part 3 Creating and Loading the Dataset.mp4 66MB
  119. 8. Introduction to PyTorch/10. Weight Initialization in PyTorch.mp4 66MB
  120. 32. Transformers/2. Input Embeddings.mp4 66MB
  121. 10. Visualize the Learning Process/6. Visualize Learning Part 6.mp4 64MB
  122. 24. Practical Neural Style Transfer in PyTorch/1. NST Practical Part 1.mp4 64MB
  123. 6. Hyperparameter Tuning and Learning Rate Scheduling/2. Step Learning Rate Decay.mp4 63MB
  124. 2. Loss Functions/8. Contrastive Loss.mp4 63MB
  125. 33. Build a Chatbot with Transformers/13. Decoder Layer.mp4 62MB
  126. 25. Recurrent Neural Networks/4. Backpropagation Through Time.mp4 62MB
  127. 14. Practical Convolutional Networks in PyTorch - Image Classification/2. Visualizing and Loading the Dataset.mp4 61MB
  128. 15. CNN Architectures/7. Seperable Convolutions.mp4 61MB
  129. 33. Build a Chatbot with Transformers/8. MultiHead Attention Implementation Part 1.mp4 60MB
  130. 7. Weight Initialization/2. What happens when all weights are initialized to the same value.mp4 60MB
  131. 27. Practical Recurrent Networks in PyTorch/1. Creating the Dictionary.mp4 60MB
  132. 11. Implementing a Neural Network from Scratch with Numpy/8. Initializing the Network.mp4 59MB
  133. 32. Transformers/4. MultiHead Attention Part 1.mp4 58MB
  134. 20. YOLO Object Detection (Theory)/12. YOLO Theory Part 12.mp4 58MB
  135. 31. Practical Sequence Modelling in PyTorch Image Captioning/5. Constructing the Dataset Part 2.mp4 57MB
  136. 35. Google Colab and Gradient Accumulation/2. Gradient Accumulation.mp4 57MB
  137. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/4. Creating the network class and the network functions.mp4 56MB
  138. 14. Practical Convolutional Networks in PyTorch - Image Classification/10. Classifying your own Handwritten images.mp4 56MB
  139. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/2. Part 2 Data Normalization.mp4 55MB
  140. 8. Introduction to PyTorch/2. Computation Graphs and Deep Learning Frameworks.mp4 55MB
  141. 26. Word Embeddings/5. Word Embeddings in PyTorch.mp4 53MB
  142. 20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11.mp4 53MB
  143. 28. Saving and Loading Models/3. Saving and Loading Part 3.mp4 53MB
  144. 14. Practical Convolutional Networks in PyTorch - Image Classification/1. Loading and Normalizing the Dataset.mp4 53MB
  145. 23. Neural Style Transfer/1. NST Theory Part 1.mp4 53MB
  146. 5. Optimization/12. Decoupling Weight Decay.mp4 52MB
  147. 1. How Neural Networks and Backpropagation Works/6. The Forward Propagation.mp4 52MB
  148. 13. Convolutional Neural Networks/3. Filters and Features.mp4 52MB
  149. 25. Recurrent Neural Networks/2. Vanilla RNNs.mp4 52MB
  150. 33. Build a Chatbot with Transformers/9. MultiHead Attention Implementation Part 2.mp4 51MB
  151. 38. GPT/5. Technical Details of GPT.mp4 51MB
  152. 36. BERT/4. Fine-tuning BERT.mp4 51MB
  153. 31. Practical Sequence Modelling in PyTorch Image Captioning/1. Implementation Details.mp4 50MB
  154. 18. Transfer Learning in PyTorch - Image Classification/5. Finetuning the Network.mp4 50MB
  155. 1. How Neural Networks and Backpropagation Works/3. The Essence of Neural Networks.mp4 50MB
  156. 5. Optimization/1. Batch Gradient Descent.mp4 49MB
  157. 11. Implementing a Neural Network from Scratch with Numpy/9. Training the Model.mp4 47MB
  158. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/6. Testing the Network.mp4 47MB
  159. 32. Transformers/1. Introduction to Transformers.mp4 47MB
  160. 13. Convolutional Neural Networks/11. CNN Characteristics.mp4 46MB
  161. 32. Transformers/5. MultiHead Attention Part 2.mp4 46MB
  162. 4. Regularization and Normalization/7. Layer Normalization.mp4 45MB
  163. 38. GPT/2. GPT Part 2.mp4 45MB
  164. 14. Practical Convolutional Networks in PyTorch - Image Classification/8. Plotting and Putting into Action.mp4 45MB
  165. 2. Loss Functions/4. Binary Cross Entropy Loss.mp4 45MB
  166. 24. Practical Neural Style Transfer in PyTorch/5. Fast Neural Style Transfer.mp4 45MB
  167. 2. Loss Functions/6. Softmax Function.mp4 45MB
  168. 15. CNN Architectures/1. CNN Architectures Part 1.mp4 44MB
  169. 33. Build a Chatbot with Transformers/17. Defining the Model.mp4 44MB
  170. 38. GPT/3. Zero-Shot Predictions with GPT.mp4 43MB
  171. 5. Optimization/5. Exponentially Weighted Average Implementation.mp4 43MB
  172. 33. Build a Chatbot with Transformers/11. Feed Forward Implementation.mp4 43MB
  173. 36. BERT/3. Next Sentence Prediction.mp4 43MB
  174. 21. Autoencoders and Variational Autoencoders/1. Autoencoders.mp4 42MB
  175. 1. How Neural Networks and Backpropagation Works/2. The Rise of Deep Learning.mp4 42MB
  176. 31. Practical Sequence Modelling in PyTorch Image Captioning/2. Utility Functions.mp4 41MB
  177. 1. How Neural Networks and Backpropagation Works/5. Gradient Descent.mp4 41MB
  178. 29. Sequence Modelling/4. How Attention Mechanisms Work.mp4 40MB
  179. 15. CNN Architectures/6. Squeeze-Excite Networks.mp4 40MB
  180. 38. GPT/4. Byte-Pair Encoding.mp4 39MB
  181. 5. Optimization/8. RMSProp.mp4 39MB
  182. 3. Activation Functions/8. Mish Activation.mp4 38MB
  183. 13. Convolutional Neural Networks/1. Prerequisite Filters.mp4 36MB
  184. 17. Transposed Convolutions/3. Transposed Convolutions.mp4 36MB
  185. 14. Practical Convolutional Networks in PyTorch - Image Classification/7. Testing the CNN.mp4 36MB
  186. 37. Vision Transformers/2. Vision Transformer Part 2.mp4 35MB
  187. 6. Hyperparameter Tuning and Learning Rate Scheduling/4. Cosine Annealing with Warm Restarts.mp4 35MB
  188. 23. Neural Style Transfer/2. NST Theory Part 2.mp4 35MB
  189. 29. Sequence Modelling/2. Image Captioning.mp4 35MB
  190. 36. BERT/1. What is BERT and its structure.mp4 35MB
  191. 31. Practical Sequence Modelling in PyTorch Image Captioning/14. Results.mp4 34MB
  192. 4. Regularization and Normalization/2. L1 and L2 Regularization.mp4 33MB
  193. 35. Google Colab and Gradient Accumulation/1. Running your models on Google Colab.mp4 33MB
  194. 32. Transformers/12. Cross Entropy Loss.mp4 33MB
  195. 10. Visualize the Learning Process/7. Neural Networks Playground.mp4 33MB
  196. 33. Build a Chatbot with Transformers/21. Action.mp4 32MB
  197. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/1. Code Details.mp4 32MB
  198. 17. Transposed Convolutions/1. Introduction to Transposed Convolutions.mp4 31MB
  199. 5. Optimization/6. Bias Correction in Exponentially Weighted Averages.mp4 31MB
  200. 38. GPT/6. Playing with HuggingFace models.mp4 30MB
  201. 21. Autoencoders and Variational Autoencoders/2. Denoising Autoencoders.mp4 30MB
  202. 13. Convolutional Neural Networks/5. More on Convolutions.mp4 30MB
  203. 1. How Neural Networks and Backpropagation Works/7. Backpropagation Part 1.mp4 29MB
  204. 15. CNN Architectures/8. Transfer Learning.mp4 29MB
  205. 30. Practical Sequence Modelling in PyTorch Chatbot Application/4. Understanding Pack Padded Sequence.mp4 29MB
  206. 32. Transformers/16. Learning Rate Warmup.mp4 29MB
  207. 2. Loss Functions/3. Huber Loss.mp4 29MB
  208. 32. Transformers/7. Residual Learning.mp4 28MB
  209. 13. Convolutional Neural Networks/7. A Tool for Convolution Visualization.mp4 28MB
  210. 1. How Neural Networks and Backpropagation Works/8. Backpropagation Part 2.mp4 28MB
  211. 11. Implementing a Neural Network from Scratch with Numpy/5. Prediction.mp4 28MB
  212. 10. Visualize the Learning Process/3. Visualize Learning Part 3.mp4 27MB
  213. 13. Convolutional Neural Networks/14. Softmax with Temperature.mp4 27MB
  214. 5. Optimization/7. Momentum.mp4 27MB
  215. 32. Transformers/10. Masked MultiHead Attention.mp4 27MB
  216. 3. Activation Functions/6. Gated Linear Units (GLU).mp4 27MB
  217. 4. Regularization and Normalization/8. Group Normalization.mp4 26MB
  218. 4. Regularization and Normalization/1. Overfitting.mp4 26MB
  219. 14. Practical Convolutional Networks in PyTorch - Image Classification/5. Understanding the Propagation.mp4 26MB
  220. 25. Recurrent Neural Networks/9. GRUs.mp4 26MB
  221. 20. YOLO Object Detection (Theory)/4. YOLO Theory Part 4.mp4 26MB
  222. 2. Loss Functions/7. KL divergence Loss.mp4 25MB
  223. 20. YOLO Object Detection (Theory)/10. YOLO Theory Part 10.mp4 25MB
  224. 13. Convolutional Neural Networks/2. Introduction to Convolutional Networks and the need for them.mp4 25MB
  225. 6. Hyperparameter Tuning and Learning Rate Scheduling/5. Batch Size vs Learning Rate.mp4 25MB
  226. 2. Loss Functions/5. Cross Entropy Loss.mp4 25MB
  227. 10. Visualize the Learning Process/1. Visualize Learning Part 1.mp4 24MB
  228. 32. Transformers/13. KL Divergence Loss.mp4 24MB
  229. 11. Implementing a Neural Network from Scratch with Numpy/2. Understanding the Implementation.mp4 23MB
  230. 36. BERT/2. Masked Language Modelling.mp4 23MB
  231. 5. Optimization/4. Exponentially Weighted Average Intuition.mp4 23MB
  232. 3. Activation Functions/1. Why we need activation functions.mp4 22MB
  233. 34. Universal Transformers/1. Universal Transformers.mp4 22MB
  234. 32. Transformers/8. Layer Normalization.mp4 22MB
  235. 30. Practical Sequence Modelling in PyTorch Chatbot Application/8. Teacher Forcing.mp4 22MB
  236. 25. Recurrent Neural Networks/10. CNN-LSTM.mp4 21MB
  237. 13. Convolutional Neural Networks/4. Convolution over Volume Animation.mp4 21MB
  238. 3. Activation Functions/4. ReLU and PReLU.mp4 21MB
  239. 33. Build a Chatbot with Transformers/4. Dataset Preprocessing Part 4.mp4 20MB
  240. 3. Activation Functions/2. Sigmoid Activation.mp4 20MB
  241. 10. Visualize the Learning Process/4. Visualize Learning Part 4.mp4 20MB
  242. 2. Loss Functions/1. Mean Squared Error (MSE).mp4 20MB
  243. 7. Weight Initialization/1. Normal Distribution.mp4 19MB
  244. 14. Practical Convolutional Networks in PyTorch - Image Classification/4. Defining the Model.mp4 19MB
  245. 25. Recurrent Neural Networks/1. Why do we need RNNs.mp4 19MB
  246. 13. Convolutional Neural Networks/12. Regularization and Batch Normalization in CNNs.mp4 18MB
  247. 5. Optimization/2. Stochastic Gradient Descent.mp4 18MB
  248. 20. YOLO Object Detection (Theory)/9. YOLO Theory Part 9.mp4 18MB
  249. 6. Hyperparameter Tuning and Learning Rate Scheduling/1. Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4 18MB
  250. 14. Practical Convolutional Networks in PyTorch - Image Classification/9. Predicting an image.mp4 17MB
  251. 29. Sequence Modelling/3. Attention Mechanisms.mp4 16MB
  252. 32. Transformers/9. Feed Forward.mp4 16MB
  253. 13. Convolutional Neural Networks/9. CNN Visualization.mp4 15MB
  254. 25. Recurrent Neural Networks/3. Quiz Solution Discussion.mp4 15MB
  255. 25. Recurrent Neural Networks/8. Bidirectional RNNs.mp4 15MB
  256. 4. Regularization and Normalization/4. DropConnect.mp4 14MB
  257. 3. Activation Functions/3. Tanh Activation.mp4 14MB
  258. 4. Regularization and Normalization/5. Normalization.mp4 14MB
  259. 21. Autoencoders and Variational Autoencoders/3. The Problem in Autoencoders.mp4 13MB
  260. 13. Convolutional Neural Networks/10. Important formulas.mp4 13MB
  261. 15. CNN Architectures/4. CNN Architectures Part 2.mp4 13MB
  262. 7. Weight Initialization/4. He Norm Initialization.mp4 13MB
  263. 32. Transformers/14. Label Smoothing.mp4 13MB
  264. 3. Activation Functions/7. Swish Activation.mp4 13MB
  265. 31. Practical Sequence Modelling in PyTorch Image Captioning/13. Training.mp4 13MB
  266. 10. Visualize the Learning Process/2. Visualize Learning Part 2.mp4 12MB
  267. 26. Word Embeddings/2. Visualizing Word Embeddings.mp4 12MB
  268. 32. Transformers/11. MultiHead Attention in Decoder.mp4 11MB
  269. 26. Word Embeddings/4. Word Embeddings Models.mp4 11MB
  270. 3. Activation Functions/5. Exponentially Linear Units (ELU).mp4 11MB
  271. 5. Optimization/10. SWATS - Switching from Adam to SGD.mp4 10MB
  272. 32. Transformers/6. Concat and Linear.mp4 10MB
  273. 25. Recurrent Neural Networks/5. Stacked RNNs.mp4 8MB
  274. 5. Optimization/3. Mini-Batch Gradient Descent.mp4 7MB
  275. 13. Convolutional Neural Networks/6. Quiz Solution Discussion.mp4 6MB
  276. 26. Word Embeddings/3. Measuring Word Embeddings.mp4 6MB
  277. 8. Introduction to PyTorch/1. CODE FOR THIS COURSE.mp4 2MB
  278. 19. Convolutional Networks Visualization/dog.jpg 93KB
  279. 21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap-en_US.srt 42KB
  280. 21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE-en_US.srt 37KB
  281. 8. Introduction to PyTorch/9. Loss Functions in PyTorch-en_US.srt 37KB
  282. 19. Convolutional Networks Visualization/imagenet-class-index.json 35KB
  283. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network-en_US.srt 33KB
  284. 14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN-en_US.srt 32KB
  285. 30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder-en_US.srt 31KB
  286. 25. Recurrent Neural Networks/7. LSTMs-en_US.srt 28KB
  287. 11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation-en_US.srt 28KB
  288. 22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1-en_US.srt 25KB
  289. 33. Build a Chatbot with Transformers/16. Loss with Label Smoothing-en_US.srt 25KB
  290. 8. Introduction to PyTorch/4. How PyTorch Works-en_US.srt 24KB
  291. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network-en_US.srt 23KB
  292. 15. CNN Architectures/3. Residual Networks Part 2-en_US.srt 23KB
  293. 31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder-en_US.srt 23KB
  294. 30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2-en_US.srt 23KB
  295. 31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1-en_US.srt 22KB
  296. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network-en_US.srt 22KB
  297. 31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function-en_US.srt 22KB
  298. 1. How Neural Networks and Backpropagation Works/4. The Perceptron-en_US.srt 21KB
  299. 11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function-en_US.srt 21KB
  300. 14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN-en_US.srt 21KB
  301. 35. Google Colab and Gradient Accumulation/2. Gradient Accumulation-en_US.srt 21KB
  302. 33. Build a Chatbot with Transformers/19. Evaluation Function-en_US.srt 21KB
  303. 31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function-en_US.srt 21KB
  304. 30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model-en_US.srt 20KB
  305. 36. BERT/5. Exploring Transformers-en_US.srt 20KB
  306. 33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2-en_US.srt 20KB
  307. 28. Saving and Loading Models/1. Saving and Loading Part 1-en_US.srt 19KB
  308. 33. Build a Chatbot with Transformers/7. Embeddings-en_US.srt 19KB
  309. 31. Practical Sequence Modelling in PyTorch Image Captioning/11. Defining Hyperparameters-en_US.srt 19KB
  310. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/1. Part 1 Data Preprocessing-en_US.srt 19KB
  311. 24. Practical Neural Style Transfer in PyTorch/4. NST Practical Part 4-en_US.srt 18KB
  312. 31. Practical Sequence Modelling in PyTorch Image Captioning/4. Constructing the Dataset Part 1-en_US.srt 18KB
  313. 1. How Neural Networks and Backpropagation Works/1. What Can Deep Learning Do-en_US.srt 18KB
  314. 31. Practical Sequence Modelling in PyTorch Image Captioning/2. Utility Functions-en_US.srt 18KB
  315. 30. Practical Sequence Modelling in PyTorch Chatbot Application/6. Designing the Decoder Part 1-en_US.srt 18KB
  316. 32. Transformers/3. Positional Encoding-en_US.srt 18KB
  317. 15. CNN Architectures/5. Densely Connected Networks-en_US.srt 18KB
  318. 19. Convolutional Networks Visualization/2. Processing the Model-en_US.srt 18KB
  319. 8. Introduction to PyTorch/2. Computation Graphs and Deep Learning Frameworks-en_US.srt 17KB
  320. 34. Universal Transformers/2. Practical Universal Transformers Modifying the Transformers code-en_US.srt 17KB
  321. 29. Sequence Modelling/1. Sequence Modeling-en_US.srt 17KB
  322. 2. Loss Functions/4. Binary Cross Entropy Loss-en_US.srt 17KB
  323. 31. Practical Sequence Modelling in PyTorch Image Captioning/9. Creating the Decoder Part 3-en_US.srt 17KB
  324. 37. Vision Transformers/1. Vision Transformer Part 1-en_US.srt 17KB
  325. 16. Practical Residual Networks in PyTorch/4. Practical ResNet Part 4-en_US.srt 17KB
  326. 13. Convolutional Neural Networks/8. Activation, Pooling and FC-en_US.srt 17KB
  327. 33. Build a Chatbot with Transformers/6. Data Loading and Masking-en_US.srt 17KB
  328. 2. Loss Functions/9. Hinge Loss-en_US.srt 17KB
  329. 25. Recurrent Neural Networks/4. Backpropagation Through Time-en_US.srt 17KB
  330. 2. Loss Functions/10. Triplet Ranking Loss-en_US.srt 16KB
  331. 8. Introduction to PyTorch/10. Weight Initialization in PyTorch-en_US.srt 16KB
  332. 19. Convolutional Networks Visualization/3. Visualizing the Feature Maps-en_US.srt 16KB
  333. 6. Hyperparameter Tuning and Learning Rate Scheduling/2. Step Learning Rate Decay-en_US.srt 16KB
  334. 27. Practical Recurrent Networks in PyTorch/6. Generating Text-en_US.srt 16KB
  335. 32. Transformers/12. Cross Entropy Loss-en_US.srt 16KB
  336. 20. YOLO Object Detection (Theory)/2. YOLO Theory Part 2-en_US.srt 16KB
  337. 33. Build a Chatbot with Transformers/10. MultiHead Attention Implementation Part 3-en_US.srt 16KB
  338. 16. Practical Residual Networks in PyTorch/2. Practical ResNet Part 2-en_US.srt 16KB
  339. 16. Practical Residual Networks in PyTorch/1. Practical ResNet Part 1-en_US.srt 16KB
  340. 2. Loss Functions/8. Contrastive Loss-en_US.srt 16KB
  341. 14. Practical Convolutional Networks in PyTorch - Image Classification/1. Loading and Normalizing the Dataset-en_US.srt 16KB
  342. 11. Implementing a Neural Network from Scratch with Numpy/6. Backpropagation Equations-en_US.srt 16KB
  343. 4. Regularization and Normalization/6. Batch Normalization-en_US.srt 16KB
  344. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/2. Importing and Defining Parameters-en_US.srt 16KB
  345. 31. Practical Sequence Modelling in PyTorch Image Captioning/1. Implementation Details-en_US.srt 16KB
  346. 32. Transformers/1. Introduction to Transformers-en_US.srt 16KB
  347. 11. Implementing a Neural Network from Scratch with Numpy/1. The Dataset and Hyperparameters-en_US.srt 16KB
  348. 16. Practical Residual Networks in PyTorch/3. Practical ResNet Part 3-en_US.srt 16KB
  349. 31. Practical Sequence Modelling in PyTorch Image Captioning/5. Constructing the Dataset Part 2-en_US.srt 16KB
  350. 18. Transfer Learning in PyTorch - Image Classification/1. Data Augmentation-en_US.srt 15KB
  351. 37. Vision Transformers/3. Vision Transformer Part 3-en_US.srt 15KB
  352. 13. Convolutional Neural Networks/13. DropBlock Dropout in CNNs-en_US.srt 15KB
  353. 22. Practical Variational Autoencoders in PyTorch/3. Practical VAE Part 3-en_US.srt 15KB
  354. 11. Implementing a Neural Network from Scratch with Numpy/3. Forward Propagation-en_US.srt 15KB
  355. 14. Practical Convolutional Networks in PyTorch - Image Classification/10. Classifying your own Handwritten images-en_US.srt 15KB
  356. 1. How Neural Networks and Backpropagation Works/5. Gradient Descent-en_US.srt 15KB
  357. 15. CNN Architectures/1. CNN Architectures Part 1-en_US.srt 15KB
  358. 5. Optimization/8. RMSProp-en_US.srt 15KB
  359. 8. Introduction to PyTorch/5. Torch Tensors - Part 1-en_US.srt 15KB
  360. 15. CNN Architectures/7. Seperable Convolutions-en_US.srt 15KB
  361. 8. Introduction to PyTorch/7. Numpy Bridge, Tensor Concatenation and Adding Dimensions-en_US.srt 15KB
  362. 33. Build a Chatbot with Transformers/14. Transformer-en_US.srt 15KB
  363. 31. Practical Sequence Modelling in PyTorch Image Captioning/8. Creating the Decoder Part 2-en_US.srt 15KB
  364. 22. Practical Variational Autoencoders in PyTorch/2. Practical VAE Part 2-en_US.srt 15KB
  365. 29. Sequence Modelling/4. How Attention Mechanisms Work-en_US.srt 15KB
  366. 18. Transfer Learning in PyTorch - Image Classification/4. Understanding the data-en_US.srt 15KB
  367. 24. Practical Neural Style Transfer in PyTorch/3. NST Practical Part 3-en_US.srt 15KB
  368. 8. Introduction to PyTorch/3. Installing PyTorch and an Introduction-en_US.srt 14KB
  369. 1. How Neural Networks and Backpropagation Works/7. Backpropagation Part 1-en_US.srt 14KB
  370. 27. Practical Recurrent Networks in PyTorch/4. Creating the Network-en_US.srt 14KB
  371. 10. Visualize the Learning Process/5. Visualize Learning Part 5-en_US.srt 14KB
  372. 15. CNN Architectures/2. Residual Networks Part 1-en_US.srt 14KB
  373. 18. Transfer Learning in PyTorch - Image Classification/2. Loading the Dataset-en_US.srt 14KB
  374. 24. Practical Neural Style Transfer in PyTorch/1. NST Practical Part 1-en_US.srt 14KB
  375. 33. Build a Chatbot with Transformers/18. Training Function-en_US.srt 14KB
  376. 33. Build a Chatbot with Transformers/3. Dataset Preprocessing Part 3-en_US.srt 14KB
  377. 21. Autoencoders and Variational Autoencoders/4. Variational Autoencoders-en_US.srt 14KB
  378. 1. How Neural Networks and Backpropagation Works/6. The Forward Propagation-en_US.srt 14KB
  379. 31. Practical Sequence Modelling in PyTorch Image Captioning/3. Accuracy Calculation-en_US.srt 14KB
  380. 38. GPT/1. GPT Part 1-en_US.srt 14KB
  381. 25. Recurrent Neural Networks/6. Vanishing and Exploding Gradient Problem-en_US.srt 14KB
  382. 23. Neural Style Transfer/3. NST Theory Part 3-en_US.srt 13KB
  383. 27. Practical Recurrent Networks in PyTorch/2. Processing the Text-en_US.srt 13KB
  384. 20. YOLO Object Detection (Theory)/12. YOLO Theory Part 12-en_US.srt 13KB
  385. 27. Practical Recurrent Networks in PyTorch/5. Training the Network-en_US.srt 13KB
  386. 33. Build a Chatbot with Transformers/1. Dataset Preprocessing Part 1-en_US.srt 13KB
  387. 15. CNN Architectures/6. Squeeze-Excite Networks-en_US.srt 13KB
  388. 8. Introduction to PyTorch/6. Torch Tensors - Part 2-en_US.srt 13KB
  389. 32. Transformers/4. MultiHead Attention Part 1-en_US.srt 13KB
  390. 6. Hyperparameter Tuning and Learning Rate Scheduling/3. Cyclic Learning Rate-en_US.srt 13KB
  391. 18. Transfer Learning in PyTorch - Image Classification/6. Testing and Visualizing the results-en_US.srt 13KB
  392. 1. How Neural Networks and Backpropagation Works/3. The Essence of Neural Networks-en_US.srt 13KB
  393. 7. Weight Initialization/2. What happens when all weights are initialized to the same value-en_US.srt 13KB
  394. 7. Weight Initialization/3. Xavier Initialization-en_US.srt 13KB
  395. 13. Convolutional Neural Networks/14. Softmax with Temperature-en_US.srt 13KB
  396. 33. Build a Chatbot with Transformers/5. Dataset Preprocessing Part 5-en_US.srt 13KB
  397. 24. Practical Neural Style Transfer in PyTorch/2. NST Practical Part 2-en_US.srt 12KB
  398. 33. Build a Chatbot with Transformers/20. Main Function and User Evaluation-en_US.srt 12KB
  399. 14. Practical Convolutional Networks in PyTorch - Image Classification/2. Visualizing and Loading the Dataset-en_US.srt 12KB
  400. 20. YOLO Object Detection (Theory)/3. YOLO Theory Part 3-en_US.srt 12KB
  401. 13. Convolutional Neural Networks/3. Filters and Features-en_US.srt 12KB
  402. 26. Word Embeddings/1. What are Word Embeddings-en_US.srt 12KB
  403. 38. GPT/2. GPT Part 2-en_US.srt 12KB
  404. 20. YOLO Object Detection (Theory)/6. YOLO Theory Part 6-en_US.srt 12KB
  405. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/3. Defining the Network Class-en_US.srt 12KB
  406. 10. Visualize the Learning Process/1. Visualize Learning Part 1-en_US.srt 12KB
  407. 1. How Neural Networks and Backpropagation Works/8. Backpropagation Part 2-en_US.srt 12KB
  408. 32. Transformers/15. Dropout-en_US.srt 12KB
  409. 4. Regularization and Normalization/3. Dropout-en_US.srt 12KB
  410. 8. Introduction to PyTorch/8. Automatic Differentiation-en_US.srt 12KB
  411. 37. Vision Transformers/2. Vision Transformer Part 2-en_US.srt 12KB
  412. 4. Regularization and Normalization/2. L1 and L2 Regularization-en_US.srt 12KB
  413. 21. Autoencoders and Variational Autoencoders/1. Autoencoders-en_US.srt 12KB
  414. 5. Optimization/13. AMSGrad-en_US.srt 12KB
  415. 15. CNN Architectures/8. Transfer Learning-en_US.srt 12KB
  416. 36. BERT/3. Next Sentence Prediction-en_US.srt 12KB
  417. 5. Optimization/5. Exponentially Weighted Average Implementation-en_US.srt 11KB
  418. 36. BERT/1. What is BERT and its structure-en_US.srt 11KB
  419. 17. Transposed Convolutions/2. Convolution Operation as Matrix Multiplication-en_US.srt 11KB
  420. 34. Universal Transformers/3. Transformers for other tasks-en_US.srt 11KB
  421. 2. Loss Functions/2. L1 Loss (MAE)-en_US.srt 11KB
  422. 11. Implementing a Neural Network from Scratch with Numpy/2. Understanding the Implementation-en_US.srt 11KB
  423. 25. Recurrent Neural Networks/2. Vanilla RNNs-en_US.srt 11KB
  424. 18. Transfer Learning in PyTorch - Image Classification/3. Modifying the Network-en_US.srt 11KB
  425. 13. Convolutional Neural Networks/11. CNN Characteristics-en_US.srt 11KB
  426. 2. Loss Functions/5. Cross Entropy Loss-en_US.srt 11KB
  427. 38. GPT/4. Byte-Pair Encoding-en_US.srt 10KB
  428. 32. Transformers/5. MultiHead Attention Part 2-en_US.srt 10KB
  429. 35. Google Colab and Gradient Accumulation/1. Running your models on Google Colab-en_US.srt 10KB
  430. 38. GPT/3. Zero-Shot Predictions with GPT-en_US.srt 10KB
  431. 33. Build a Chatbot with Transformers/9. MultiHead Attention Implementation Part 2-en_US.srt 10KB
  432. 10. Visualize the Learning Process/3. Visualize Learning Part 3-en_US.srt 10KB
  433. 20. YOLO Object Detection (Theory)/5. YOLO Theory Part 5-en_US.srt 10KB
  434. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/2. Part 2 Data Normalization-en_US.srt 10KB
  435. 19. Convolutional Networks Visualization/1. Data and the Model-en_US.srt 10KB
  436. 4. Regularization and Normalization/7. Layer Normalization-en_US.srt 10KB
  437. 21. Autoencoders and Variational Autoencoders/7. Deep Fake-en_US.srt 10KB
  438. 28. Saving and Loading Models/2. Saving and Loading Part 2-en_US.srt 10KB
  439. 10. Visualize the Learning Process/6. Visualize Learning Part 6-en_US.srt 10KB
  440. 2. Loss Functions/6. Softmax Function-en_US.srt 10KB
  441. 33. Build a Chatbot with Transformers/12. Encoder Layer-en_US.srt 10KB
  442. 30. Practical Sequence Modelling in PyTorch Chatbot Application/4. Understanding Pack Padded Sequence-en_US.srt 10KB
  443. 38. GPT/6. Playing with HuggingFace models-en_US.srt 10KB
  444. 2. Loss Functions/7. KL divergence Loss-en_US.srt 10KB
  445. 27. Practical Recurrent Networks in PyTorch/3. Defining and Visualizing the Parameters-en_US.srt 10KB
  446. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/3. Part 3 Creating and Loading the Dataset-en_US.srt 9KB
  447. 32. Transformers/8. Layer Normalization-en_US.srt 9KB
  448. 5. Optimization/9. Adam Optimization-en_US.srt 9KB
  449. 34. Universal Transformers/1. Universal Transformers-en_US.srt 9KB
  450. 5. Optimization/11. Weight Decay-en_US.srt 9KB
  451. 21. Autoencoders and Variational Autoencoders/2. Denoising Autoencoders-en_US.srt 9KB
  452. 13. Convolutional Neural Networks/2. Introduction to Convolutional Networks and the need for them-en_US.srt 9KB
  453. 2. Loss Functions/1. Mean Squared Error (MSE)-en_US.srt 9KB
  454. 23. Neural Style Transfer/1. NST Theory Part 1-en_US.srt 9KB
  455. 3. Activation Functions/4. ReLU and PReLU-en_US.srt 9KB
  456. 36. BERT/4. Fine-tuning BERT-en_US.srt 9KB
  457. 38. GPT/5. Technical Details of GPT-en_US.srt 9KB
  458. 17. Transposed Convolutions/1. Introduction to Transposed Convolutions-en_US.srt 9KB
  459. 25. Recurrent Neural Networks/9. GRUs-en_US.srt 9KB
  460. 14. Practical Convolutional Networks in PyTorch - Image Classification/7. Testing the CNN-en_US.srt 9KB
  461. 20. YOLO Object Detection (Theory)/7. YOLO Theory Part 7-en_US.srt 9KB
  462. 33. Build a Chatbot with Transformers/15. AdamWarmup-en_US.srt 9KB
  463. 33. Build a Chatbot with Transformers/8. MultiHead Attention Implementation Part 1-en_US.srt 9KB
  464. 32. Transformers/10. Masked MultiHead Attention-en_US.srt 9KB
  465. 13. Convolutional Neural Networks/5. More on Convolutions-en_US.srt 9KB
  466. 20. YOLO Object Detection (Theory)/4. YOLO Theory Part 4-en_US.srt 9KB
  467. 7. Weight Initialization/1. Normal Distribution-en_US.srt 9KB
  468. 32. Transformers/16. Learning Rate Warmup-en_US.srt 9KB
  469. 32. Transformers/2. Input Embeddings-en_US.srt 9KB
  470. 32. Transformers/7. Residual Learning-en_US.srt 8KB
  471. 33. Build a Chatbot with Transformers/17. Defining the Model-en_US.srt 8KB
  472. 17. Transposed Convolutions/3. Transposed Convolutions-en_US.srt 8KB
  473. 11. Implementing a Neural Network from Scratch with Numpy/8. Initializing the Network-en_US.srt 8KB
  474. 5. Optimization/1. Batch Gradient Descent-en_US.srt 8KB
  475. 3. Activation Functions/2. Sigmoid Activation-en_US.srt 8KB
  476. 2. Loss Functions/3. Huber Loss-en_US.srt 8KB
  477. 1. How Neural Networks and Backpropagation Works/2. The Rise of Deep Learning-en_US.srt 8KB
  478. 5. Optimization/6. Bias Correction in Exponentially Weighted Averages-en_US.srt 8KB
  479. 23. Neural Style Transfer/2. NST Theory Part 2-en_US.srt 8KB
  480. 4. Regularization and Normalization/8. Group Normalization-en_US.srt 8KB
  481. 26. Word Embeddings/5. Word Embeddings in PyTorch-en_US.srt 8KB
  482. 30. Practical Sequence Modelling in PyTorch Chatbot Application/1. Introduction-en_US.srt 8KB
  483. 30. Practical Sequence Modelling in PyTorch Chatbot Application/2. Understanding the Encoder-en_US.srt 8KB
  484. 32. Transformers/13. KL Divergence Loss-en_US.srt 8KB
  485. 5. Optimization/7. Momentum-en_US.srt 8KB
  486. 14. Practical Convolutional Networks in PyTorch - Image Classification/5. Understanding the Propagation-en_US.srt 8KB
  487. 27. Practical Recurrent Networks in PyTorch/1. Creating the Dictionary-en_US.srt 8KB
  488. 28. Saving and Loading Models/3. Saving and Loading Part 3-en_US.srt 8KB
  489. 3. Activation Functions/8. Mish Activation-en_US.srt 8KB
  490. 20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11-en_US.srt 7KB
  491. 6. Hyperparameter Tuning and Learning Rate Scheduling/4. Cosine Annealing with Warm Restarts-en_US.srt 7KB
  492. 36. BERT/2. Masked Language Modelling-en_US.srt 7KB
  493. 20. YOLO Object Detection (Theory)/8. YOLO Theory Part 8-en_US.srt 7KB
  494. 10. Visualize the Learning Process/4. Visualize Learning Part 4-en_US.srt 7KB
  495. 29. Sequence Modelling/3. Attention Mechanisms-en_US.srt 7KB
  496. 11. Implementing a Neural Network from Scratch with Numpy/5. Prediction-en_US.srt 7KB
  497. 5. Optimization/4. Exponentially Weighted Average Intuition-en_US.srt 7KB
  498. 13. Convolutional Neural Networks/10. Important formulas-en_US.srt 7KB
  499. 18. Transfer Learning in PyTorch - Image Classification/5. Finetuning the Network-en_US.srt 7KB
  500. 10. Visualize the Learning Process/7. Neural Networks Playground-en_US.srt 7KB
  501. 33. Build a Chatbot with Transformers/13. Decoder Layer-en_US.srt 7KB
  502. 20. YOLO Object Detection (Theory)/1. YOLO Theory Part 1-en_US.srt 7KB
  503. 25. Recurrent Neural Networks/1. Why do we need RNNs-en_US.srt 7KB
  504. 29. Sequence Modelling/2. Image Captioning-en_US.srt 7KB
  505. 6. Hyperparameter Tuning and Learning Rate Scheduling/1. Introduction to Hyperparameter Tuning and Learning Rate Recap-en_US.srt 7KB
  506. 5. Optimization/2. Stochastic Gradient Descent-en_US.srt 7KB
  507. 30. Practical Sequence Modelling in PyTorch Chatbot Application/8. Teacher Forcing-en_US.srt 6KB
  508. 21. Autoencoders and Variational Autoencoders/3. The Problem in Autoencoders-en_US.srt 6KB
  509. 4. Regularization and Normalization/1. Overfitting-en_US.srt 6KB
  510. 14. Practical Convolutional Networks in PyTorch - Image Classification/8. Plotting and Putting into Action-en_US.srt 6KB
  511. 25. Recurrent Neural Networks/10. CNN-LSTM-en_US.srt 6KB
  512. 14. Practical Convolutional Networks in PyTorch - Image Classification/9. Predicting an image-en_US.srt 6KB
  513. 13. Convolutional Neural Networks/1. Prerequisite Filters-en_US.srt 6KB
  514. 4. Regularization and Normalization/5. Normalization-en_US.srt 6KB
  515. 13. Convolutional Neural Networks/7. A Tool for Convolution Visualization-en_US.srt 6KB
  516. 32. Transformers/14. Label Smoothing-en_US.srt 6KB
  517. 5. Optimization/12. Decoupling Weight Decay-en_US.srt 6KB
  518. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/6. Testing the Network-en_US.srt 6KB
  519. 33. Build a Chatbot with Transformers/4. Dataset Preprocessing Part 4-en_US.srt 6KB
  520. 14. Practical Convolutional Networks in PyTorch - Image Classification/4. Defining the Model-en_US.srt 6KB
  521. 11. Implementing a Neural Network from Scratch with Numpy/9. Training the Model-en_US.srt 5KB
  522. 20. YOLO Object Detection (Theory)/9. YOLO Theory Part 9-en_US.srt 5KB
  523. 25. Recurrent Neural Networks/8. Bidirectional RNNs-en_US.srt 5KB
  524. 3. Activation Functions/7. Swish Activation-en_US.srt 5KB
  525. 24. Practical Neural Style Transfer in PyTorch/5. Fast Neural Style Transfer-en_US.srt 5KB
  526. 25. Recurrent Neural Networks/3. Quiz Solution Discussion-en_US.srt 5KB
  527. 3. Activation Functions/1. Why we need activation functions-en_US.srt 5KB
  528. 7. Weight Initialization/4. He Norm Initialization-en_US.srt 5KB
  529. 3. Activation Functions/5. Exponentially Linear Units (ELU)-en_US.srt 5KB
  530. 13. Convolutional Neural Networks/12. Regularization and Batch Normalization in CNNs-en_US.srt 5KB
  531. 15. CNN Architectures/4. CNN Architectures Part 2-en_US.srt 5KB
  532. 13. Convolutional Neural Networks/4. Convolution over Volume Animation-en_US.srt 5KB
  533. 13. Convolutional Neural Networks/6. Quiz Solution Discussion-en_US.srt 4KB
  534. 33. Build a Chatbot with Transformers/11. Feed Forward Implementation-en_US.srt 4KB
  535. 26. Word Embeddings/2. Visualizing Word Embeddings-en_US.srt 4KB
  536. 32. Transformers/9. Feed Forward-en_US.srt 4KB
  537. 26. Word Embeddings/4. Word Embeddings Models-en_US.srt 4KB
  538. 3. Activation Functions/3. Tanh Activation-en_US.srt 4KB
  539. 6. Hyperparameter Tuning and Learning Rate Scheduling/5. Batch Size vs Learning Rate-en_US.srt 4KB
  540. 32. Transformers/6. Concat and Linear-en_US.srt 4KB
  541. 3. Activation Functions/6. Gated Linear Units (GLU)-en_US.srt 4KB
  542. 33. Build a Chatbot with Transformers/21. Action-en_US.srt 4KB
  543. 31. Practical Sequence Modelling in PyTorch Image Captioning/14. Results-en_US.srt 4KB
  544. 25. Recurrent Neural Networks/5. Stacked RNNs-en_US.srt 3KB
  545. 32. Transformers/11. MultiHead Attention in Decoder-en_US.srt 3KB
  546. 5. Optimization/3. Mini-Batch Gradient Descent-en_US.srt 3KB
  547. 31. Practical Sequence Modelling in PyTorch Image Captioning/13. Training-en_US.srt 3KB
  548. 20. YOLO Object Detection (Theory)/10. YOLO Theory Part 10-en_US.srt 3KB
  549. 13. Convolutional Neural Networks/9. CNN Visualization-en_US.srt 3KB
  550. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/1. Code Details-en_US.srt 3KB
  551. 26. Word Embeddings/3. Measuring Word Embeddings-en_US.srt 3KB
  552. 10. Visualize the Learning Process/2. Visualize Learning Part 2-en_US.srt 2KB
  553. 4. Regularization and Normalization/4. DropConnect-en_US.srt 2KB
  554. 5. Optimization/10. SWATS - Switching from Adam to SGD-en_US.srt 2KB
  555. 20. YOLO Object Detection (Theory)/YOLO Code Note.html 1KB
  556. 8. Introduction to PyTorch/1. CODE FOR THIS COURSE-en_US.srt 701B
  557. 1. How Neural Networks and Backpropagation Works/BEFORE STARTING...PLEASE READ THIS.html 630B
  558. 11. Implementing a Neural Network from Scratch with Numpy/Notebook for the following Lecture.html 532B
  559. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/The MNIST Dataset.html 421B
  560. 2. Loss Functions/Softmax with Temperature Controlling your distribution.html 394B
  561. 4. Regularization and Normalization/Note on Weight Decay.html 354B
  562. 1. How Neural Networks and Backpropagation Works/Before Proceeding with the Backpropagation.html 341B
  563. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/Download the Dataset.html 322B
  564. 13. Convolutional Neural Networks/Convolution over Volume Animation Resource.html 321B
  565. 27. Practical Recurrent Networks in PyTorch/Download the Dataset.html 312B
  566. 32. Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html 272B
  567. 33. Build a Chatbot with Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html 272B
  568. 34. Universal Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html 272B
  569. 37. Vision Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html 272B
  570. 33. Build a Chatbot with Transformers/CODE.html 268B
  571. 4. Regularization and Normalization/DropBlock in CNNs.html 256B
  572. 30. Practical Sequence Modelling in PyTorch Chatbot Application/Download the Dataset.html 252B
  573. 7. Weight Initialization/Practical Weight Initialization Note.html 186B
  574. 2. Loss Functions/Practical Loss Functions Note.html 179B
  575. 18. Transfer Learning in PyTorch - Image Classification/[Tutorialsplanet.NET].url 128B
  576. 2. Loss Functions/[Tutorialsplanet.NET].url 128B
  577. 25. Recurrent Neural Networks/[Tutorialsplanet.NET].url 128B
  578. 32. Transformers/[Tutorialsplanet.NET].url 128B
  579. 8. Introduction to PyTorch/[Tutorialsplanet.NET].url 128B
  580. [Tutorialsplanet.NET].url 128B
  581. 15. CNN Architectures/Note on Residual Networks Implementation.html 109B
  582. 38. GPT/Implementation.html 87B
  583. 18. Transfer Learning in PyTorch - Image Classification/2. External URLs.txt 70B
  584. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/4. Creating the network class and the network functions-en_US.srt 0B