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

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Tutorialsplanet NET Udemy - The Complete Neural Networks Bootcamp Theory Applications

文件列表
  1. 1. How Neural Networks and Backpropagation Works/1. What Can Deep Learning Do.mp4156.25MB
  2. 1. How Neural Networks and Backpropagation Works/2. The Rise of Deep Learning.mp441.8MB
  3. 1. How Neural Networks and Backpropagation Works/3. The Essence of Neural Networks.mp449.99MB
  4. 1. How Neural Networks and Backpropagation Works/4. The Perceptron.mp4110.88MB
  5. 1. How Neural Networks and Backpropagation Works/5. Gradient Descent.mp440.6MB
  6. 1. How Neural Networks and Backpropagation Works/6. The Forward Propagation.mp452.23MB
  7. 1. How Neural Networks and Backpropagation Works/7. Backpropagation Part 1.mp429.37MB
  8. 1. How Neural Networks and Backpropagation Works/8. Backpropagation Part 2.mp427.82MB
  9. 10. Visualize the Learning Process/1. Visualize Learning Part 1.mp424.38MB
  10. 10. Visualize the Learning Process/2. Visualize Learning Part 2.mp412.21MB
  11. 10. Visualize the Learning Process/3. Visualize Learning Part 3.mp427.37MB
  12. 10. Visualize the Learning Process/4. Visualize Learning Part 4.mp420.1MB
  13. 10. Visualize the Learning Process/5. Visualize Learning Part 5.mp471.66MB
  14. 10. Visualize the Learning Process/6. Visualize Learning Part 6.mp464.39MB
  15. 10. Visualize the Learning Process/7. Neural Networks Playground.mp432.52MB
  16. 11. Implementing a Neural Network from Scratch with Numpy/1. The Dataset and Hyperparameters.mp470.53MB
  17. 11. Implementing a Neural Network from Scratch with Numpy/2. Understanding the Implementation.mp423.4MB
  18. 11. Implementing a Neural Network from Scratch with Numpy/3. Forward Propagation.mp485.2MB
  19. 11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function.mp468.48MB
  20. 11. Implementing a Neural Network from Scratch with Numpy/5. Prediction.mp427.71MB
  21. 11. Implementing a Neural Network from Scratch with Numpy/6. Backpropagation Equations.mp498.77MB
  22. 11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation.mp4148.09MB
  23. 11. Implementing a Neural Network from Scratch with Numpy/8. Initializing the Network.mp458.9MB
  24. 11. Implementing a Neural Network from Scratch with Numpy/9. Training the Model.mp447.19MB
  25. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/1. Code Details.mp431.94MB
  26. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/2. Importing and Defining Parameters.mp4142.18MB
  27. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/3. Defining the Network Class.mp485.95MB
  28. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/4. Creating the network class and the network functions.mp456.2MB
  29. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network.mp4333.24MB
  30. 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/6. Testing the Network.mp447.1MB
  31. 13. Convolutional Neural Networks/1. Prerequisite Filters.mp436.41MB
  32. 13. Convolutional Neural Networks/10. Important formulas.mp413.38MB
  33. 13. Convolutional Neural Networks/11. CNN Characteristics.mp445.88MB
  34. 13. Convolutional Neural Networks/12. Regularization and Batch Normalization in CNNs.mp418.19MB
  35. 13. Convolutional Neural Networks/13. DropBlock Dropout in CNNs.mp499.51MB
  36. 13. Convolutional Neural Networks/14. Softmax with Temperature.mp427.35MB
  37. 13. Convolutional Neural Networks/2. Introduction to Convolutional Networks and the need for them.mp425.12MB
  38. 13. Convolutional Neural Networks/3. Filters and Features.mp451.93MB
  39. 13. Convolutional Neural Networks/4. Convolution over Volume Animation.mp421.31MB
  40. 13. Convolutional Neural Networks/5. More on Convolutions.mp429.98MB
  41. 13. Convolutional Neural Networks/6. Quiz Solution Discussion.mp45.87MB
  42. 13. Convolutional Neural Networks/7. A Tool for Convolution Visualization.mp427.97MB
  43. 13. Convolutional Neural Networks/8. Activation, Pooling and FC.mp480.68MB
  44. 13. Convolutional Neural Networks/9. CNN Visualization.mp415.41MB
  45. 14. Practical Convolutional Networks in PyTorch - Image Classification/1. Loading and Normalizing the Dataset.mp452.57MB
  46. 14. Practical Convolutional Networks in PyTorch - Image Classification/10. Classifying your own Handwritten images.mp455.66MB
  47. 14. Practical Convolutional Networks in PyTorch - Image Classification/2. Visualizing and Loading the Dataset.mp460.74MB
  48. 14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN.mp4251.43MB
  49. 14. Practical Convolutional Networks in PyTorch - Image Classification/4. Defining the Model.mp418.68MB
  50. 14. Practical Convolutional Networks in PyTorch - Image Classification/5. Understanding the Propagation.mp426.19MB
  51. 14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN.mp4131.06MB
  52. 14. Practical Convolutional Networks in PyTorch - Image Classification/7. Testing the CNN.mp435.82MB
  53. 14. Practical Convolutional Networks in PyTorch - Image Classification/8. Plotting and Putting into Action.mp445.32MB
  54. 14. Practical Convolutional Networks in PyTorch - Image Classification/9. Predicting an image.mp417.46MB
  55. 15. CNN Architectures/1. CNN Architectures Part 1.mp443.87MB
  56. 15. CNN Architectures/2. Residual Networks Part 1.mp4122.27MB
  57. 15. CNN Architectures/3. Residual Networks Part 2.mp4151.37MB
  58. 15. CNN Architectures/4. CNN Architectures Part 2.mp413.38MB
  59. 15. CNN Architectures/5. Densely Connected Networks.mp495.14MB
  60. 15. CNN Architectures/6. Squeeze-Excite Networks.mp439.6MB
  61. 15. CNN Architectures/7. Seperable Convolutions.mp460.51MB
  62. 15. CNN Architectures/8. Transfer Learning.mp429.24MB
  63. 16. Practical Residual Networks in PyTorch/1. Practical ResNet Part 1.mp471.51MB
  64. 16. Practical Residual Networks in PyTorch/2. Practical ResNet Part 2.mp485.73MB
  65. 16. Practical Residual Networks in PyTorch/3. Practical ResNet Part 3.mp4103.17MB
  66. 16. Practical Residual Networks in PyTorch/4. Practical ResNet Part 4.mp4143.28MB
  67. 17. Transposed Convolutions/1. Introduction to Transposed Convolutions.mp430.98MB
  68. 17. Transposed Convolutions/2. Convolution Operation as Matrix Multiplication.mp470.98MB
  69. 17. Transposed Convolutions/3. Transposed Convolutions.mp436.09MB
  70. 18. Transfer Learning in PyTorch - Image Classification/1. Data Augmentation.mp4224.61MB
  71. 18. Transfer Learning in PyTorch - Image Classification/2. Loading the Dataset.mp4177.38MB
  72. 18. Transfer Learning in PyTorch - Image Classification/3. Modifying the Network.mp496.99MB
  73. 18. Transfer Learning in PyTorch - Image Classification/4. Understanding the data.mp4101.76MB
  74. 18. Transfer Learning in PyTorch - Image Classification/5. Finetuning the Network.mp450.02MB
  75. 18. Transfer Learning in PyTorch - Image Classification/6. Testing and Visualizing the results.mp4118.43MB
  76. 19. Convolutional Networks Visualization/1. Data and the Model.mp474.39MB
  77. 19. Convolutional Networks Visualization/2. Processing the Model.mp4142.48MB
  78. 19. Convolutional Networks Visualization/3. Visualizing the Feature Maps.mp4133.26MB
  79. 2. Loss Functions/1. Mean Squared Error (MSE).mp419.82MB
  80. 2. Loss Functions/10. Triplet Ranking Loss.mp4125.7MB
  81. 2. Loss Functions/2. L1 Loss (MAE).mp477.21MB
  82. 2. Loss Functions/3. Huber Loss.mp428.65MB
  83. 2. Loss Functions/4. Binary Cross Entropy Loss.mp444.94MB
  84. 2. Loss Functions/5. Cross Entropy Loss.mp424.66MB
  85. 2. Loss Functions/6. Softmax Function.mp444.73MB
  86. 2. Loss Functions/7. KL divergence Loss.mp425.4MB
  87. 2. Loss Functions/8. Contrastive Loss.mp462.66MB
  88. 2. Loss Functions/9. Hinge Loss.mp467.43MB
  89. 20. YOLO Object Detection (Theory)/1. YOLO Theory Part 1.mp4133.82MB
  90. 20. YOLO Object Detection (Theory)/10. YOLO Theory Part 10.mp425.29MB
  91. 20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11.mp452.8MB
  92. 20. YOLO Object Detection (Theory)/12. YOLO Theory Part 12.mp458.28MB
  93. 20. YOLO Object Detection (Theory)/2. YOLO Theory Part 2.mp480.65MB
  94. 20. YOLO Object Detection (Theory)/3. YOLO Theory Part 3.mp4123.91MB
  95. 20. YOLO Object Detection (Theory)/4. YOLO Theory Part 4.mp425.77MB
  96. 20. YOLO Object Detection (Theory)/5. YOLO Theory Part 5.mp4104.97MB
  97. 20. YOLO Object Detection (Theory)/6. YOLO Theory Part 6.mp4123.77MB
  98. 20. YOLO Object Detection (Theory)/7. YOLO Theory Part 7.mp469.72MB
  99. 20. YOLO Object Detection (Theory)/8. YOLO Theory Part 8.mp477.19MB
  100. 20. YOLO Object Detection (Theory)/9. YOLO Theory Part 9.mp417.69MB
  101. 21. Autoencoders and Variational Autoencoders/1. Autoencoders.mp442.08MB
  102. 21. Autoencoders and Variational Autoencoders/2. Denoising Autoencoders.mp430MB
  103. 21. Autoencoders and Variational Autoencoders/3. The Problem in Autoencoders.mp413.42MB
  104. 21. Autoencoders and Variational Autoencoders/4. Variational Autoencoders.mp470.2MB
  105. 21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap.mp4259.26MB
  106. 21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE.mp4319.16MB
  107. 21. Autoencoders and Variational Autoencoders/7. Deep Fake.mp485.25MB
  108. 22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1.mp4101.17MB
  109. 22. Practical Variational Autoencoders in PyTorch/2. Practical VAE Part 2.mp4103.79MB
  110. 22. Practical Variational Autoencoders in PyTorch/3. Practical VAE Part 3.mp493.22MB
  111. 23. Neural Style Transfer/1. NST Theory Part 1.mp452.53MB
  112. 23. Neural Style Transfer/2. NST Theory Part 2.mp435.19MB
  113. 23. Neural Style Transfer/3. NST Theory Part 3.mp469.11MB
  114. 24. Practical Neural Style Transfer in PyTorch/1. NST Practical Part 1.mp463.78MB
  115. 24. Practical Neural Style Transfer in PyTorch/2. NST Practical Part 2.mp4127.87MB
  116. 24. Practical Neural Style Transfer in PyTorch/3. NST Practical Part 3.mp4105.89MB
  117. 24. Practical Neural Style Transfer in PyTorch/4. NST Practical Part 4.mp4130.96MB
  118. 24. Practical Neural Style Transfer in PyTorch/5. Fast Neural Style Transfer.mp444.83MB
  119. 25. Recurrent Neural Networks/1. Why do we need RNNs.mp418.62MB
  120. 25. Recurrent Neural Networks/10. CNN-LSTM.mp421.45MB
  121. 25. Recurrent Neural Networks/2. Vanilla RNNs.mp451.57MB
  122. 25. Recurrent Neural Networks/3. Quiz Solution Discussion.mp415.38MB
  123. 25. Recurrent Neural Networks/4. Backpropagation Through Time.mp461.56MB
  124. 25. Recurrent Neural Networks/5. Stacked RNNs.mp47.77MB
  125. 25. Recurrent Neural Networks/6. Vanishing and Exploding Gradient Problem.mp466.86MB
  126. 25. Recurrent Neural Networks/7. LSTMs.mp4111.65MB
  127. 25. Recurrent Neural Networks/8. Bidirectional RNNs.mp415.03MB
  128. 25. Recurrent Neural Networks/9. GRUs.mp426.15MB
  129. 26. Word Embeddings/1. What are Word Embeddings.mp472.71MB
  130. 26. Word Embeddings/2. Visualizing Word Embeddings.mp412.19MB
  131. 26. Word Embeddings/3. Measuring Word Embeddings.mp45.53MB
  132. 26. Word Embeddings/4. Word Embeddings Models.mp410.65MB
  133. 26. Word Embeddings/5. Word Embeddings in PyTorch.mp453.24MB
  134. 27. Practical Recurrent Networks in PyTorch/1. Creating the Dictionary.mp459.88MB
  135. 27. Practical Recurrent Networks in PyTorch/2. Processing the Text.mp4108.66MB
  136. 27. Practical Recurrent Networks in PyTorch/3. Defining and Visualizing the Parameters.mp469.54MB
  137. 27. Practical Recurrent Networks in PyTorch/4. Creating the Network.mp4112.1MB
  138. 27. Practical Recurrent Networks in PyTorch/5. Training the Network.mp4151.65MB
  139. 27. Practical Recurrent Networks in PyTorch/6. Generating Text.mp4177.83MB
  140. 28. Saving and Loading Models/1. Saving and Loading Part 1.mp4130.61MB
  141. 28. Saving and Loading Models/2. Saving and Loading Part 2.mp496.57MB
  142. 28. Saving and Loading Models/3. Saving and Loading Part 3.mp452.79MB
  143. 29. Sequence Modelling/1. Sequence Modeling.mp481.57MB
  144. 29. Sequence Modelling/2. Image Captioning.mp434.74MB
  145. 29. Sequence Modelling/3. Attention Mechanisms.mp416.49MB
  146. 29. Sequence Modelling/4. How Attention Mechanisms Work.mp440.15MB
  147. 3. Activation Functions/1. Why we need activation functions.mp422.45MB
  148. 3. Activation Functions/2. Sigmoid Activation.mp420.16MB
  149. 3. Activation Functions/3. Tanh Activation.mp413.87MB
  150. 3. Activation Functions/4. ReLU and PReLU.mp420.77MB
  151. 3. Activation Functions/5. Exponentially Linear Units (ELU).mp410.64MB
  152. 3. Activation Functions/6. Gated Linear Units (GLU).mp426.52MB
  153. 3. Activation Functions/7. Swish Activation.mp412.87MB
  154. 3. Activation Functions/8. Mish Activation.mp438.14MB
  155. 30. Practical Sequence Modelling in PyTorch Chatbot Application/1. Introduction.mp474.44MB
  156. 30. Practical Sequence Modelling in PyTorch Chatbot Application/2. Understanding the Encoder.mp492.74MB
  157. 30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder.mp4404.31MB
  158. 30. Practical Sequence Modelling in PyTorch Chatbot Application/4. Understanding Pack Padded Sequence.mp429.21MB
  159. 30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model.mp4260.29MB
  160. 30. Practical Sequence Modelling in PyTorch Chatbot Application/6. Designing the Decoder Part 1.mp4139.29MB
  161. 30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2.mp4176.14MB
  162. 30. Practical Sequence Modelling in PyTorch Chatbot Application/8. Teacher Forcing.mp421.72MB
  163. 31. Practical Sequence Modelling in PyTorch Image Captioning/1. Implementation Details.mp450.34MB
  164. 31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function.mp4158.91MB
  165. 31. Practical Sequence Modelling in PyTorch Image Captioning/11. Defining Hyperparameters.mp4104.79MB
  166. 31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function.mp490.6MB
  167. 31. Practical Sequence Modelling in PyTorch Image Captioning/13. Training.mp412.85MB
  168. 31. Practical Sequence Modelling in PyTorch Image Captioning/14. Results.mp433.86MB
  169. 31. Practical Sequence Modelling in PyTorch Image Captioning/2. Utility Functions.mp441.36MB
  170. 31. Practical Sequence Modelling in PyTorch Image Captioning/3. Accuracy Calculation.mp474.06MB
  171. 31. Practical Sequence Modelling in PyTorch Image Captioning/4. Constructing the Dataset Part 1.mp4136.13MB
  172. 31. Practical Sequence Modelling in PyTorch Image Captioning/5. Constructing the Dataset Part 2.mp456.91MB
  173. 31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder.mp484.85MB
  174. 31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1.mp4118.19MB
  175. 31. Practical Sequence Modelling in PyTorch Image Captioning/8. Creating the Decoder Part 2.mp497.47MB
  176. 31. Practical Sequence Modelling in PyTorch Image Captioning/9. Creating the Decoder Part 3.mp4131.05MB
  177. 32. Transformers/1. Introduction to Transformers.mp446.69MB
  178. 32. Transformers/10. Masked MultiHead Attention.mp426.69MB
  179. 32. Transformers/11. MultiHead Attention in Decoder.mp411.07MB
  180. 32. Transformers/12. Cross Entropy Loss.mp432.68MB
  181. 32. Transformers/13. KL Divergence Loss.mp423.59MB
  182. 32. Transformers/14. Label Smoothing.mp413.21MB
  183. 32. Transformers/15. Dropout.mp475.25MB
  184. 32. Transformers/16. Learning Rate Warmup.mp429.07MB
  185. 32. Transformers/2. Input Embeddings.mp465.76MB
  186. 32. Transformers/3. Positional Encoding.mp495.97MB
  187. 32. Transformers/4. MultiHead Attention Part 1.mp458.32MB
  188. 32. Transformers/5. MultiHead Attention Part 2.mp445.85MB
  189. 32. Transformers/6. Concat and Linear.mp49.77MB
  190. 32. Transformers/7. Residual Learning.mp428.02MB
  191. 32. Transformers/8. Layer Normalization.mp421.79MB
  192. 32. Transformers/9. Feed Forward.mp415.53MB
  193. 33. Build a Chatbot with Transformers/1. Dataset Preprocessing Part 1.mp483.35MB
  194. 33. Build a Chatbot with Transformers/10. MultiHead Attention Implementation Part 3.mp4123.48MB
  195. 33. Build a Chatbot with Transformers/11. Feed Forward Implementation.mp442.91MB
  196. 33. Build a Chatbot with Transformers/12. Encoder Layer.mp486.66MB
  197. 33. Build a Chatbot with Transformers/13. Decoder Layer.mp462.27MB
  198. 33. Build a Chatbot with Transformers/14. Transformer.mp4117.13MB
  199. 33. Build a Chatbot with Transformers/15. AdamWarmup.mp475.29MB
  200. 33. Build a Chatbot with Transformers/16. Loss with Label Smoothing.mp4214.69MB
  201. 33. Build a Chatbot with Transformers/17. Defining the Model.mp443.71MB
  202. 33. Build a Chatbot with Transformers/18. Training Function.mp4100.55MB
  203. 33. Build a Chatbot with Transformers/19. Evaluation Function.mp4109.81MB
  204. 33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2.mp4134.64MB
  205. 33. Build a Chatbot with Transformers/20. Main Function and User Evaluation.mp493.28MB
  206. 33. Build a Chatbot with Transformers/21. Action.mp432.24MB
  207. 33. Build a Chatbot with Transformers/3. Dataset Preprocessing Part 3.mp480.05MB
  208. 33. Build a Chatbot with Transformers/4. Dataset Preprocessing Part 4.mp420.34MB
  209. 33. Build a Chatbot with Transformers/5. Dataset Preprocessing Part 5.mp492.39MB
  210. 33. Build a Chatbot with Transformers/6. Data Loading and Masking.mp475.82MB
  211. 33. Build a Chatbot with Transformers/7. Embeddings.mp481.22MB
  212. 33. Build a Chatbot with Transformers/8. MultiHead Attention Implementation Part 1.mp460.43MB
  213. 33. Build a Chatbot with Transformers/9. MultiHead Attention Implementation Part 2.mp451.41MB
  214. 34. Universal Transformers/1. Universal Transformers.mp421.83MB
  215. 34. Universal Transformers/2. Practical Universal Transformers Modifying the Transformers code.mp4161.1MB
  216. 34. Universal Transformers/3. Transformers for other tasks.mp4112.79MB
  217. 35. Google Colab and Gradient Accumulation/1. Running your models on Google Colab.mp433.18MB
  218. 35. Google Colab and Gradient Accumulation/2. Gradient Accumulation.mp456.83MB
  219. 36. BERT/1. What is BERT and its structure.mp434.67MB
  220. 36. BERT/2. Masked Language Modelling.mp423.09MB
  221. 36. BERT/3. Next Sentence Prediction.mp442.59MB
  222. 36. BERT/4. Fine-tuning BERT.mp450.66MB
  223. 36. BERT/5. Exploring Transformers.mp4136.61MB
  224. 37. Vision Transformers/1. Vision Transformer Part 1.mp485.28MB
  225. 37. Vision Transformers/2. Vision Transformer Part 2.mp435.31MB
  226. 37. Vision Transformers/3. Vision Transformer Part 3.mp4106.39MB
  227. 38. GPT/1. GPT Part 1.mp488.85MB
  228. 38. GPT/2. GPT Part 2.mp445.39MB
  229. 38. GPT/3. Zero-Shot Predictions with GPT.mp443.41MB
  230. 38. GPT/4. Byte-Pair Encoding.mp439.26MB
  231. 38. GPT/5. Technical Details of GPT.mp451.4MB
  232. 38. GPT/6. Playing with HuggingFace models.mp430.23MB
  233. 4. Regularization and Normalization/1. Overfitting.mp426.27MB
  234. 4. Regularization and Normalization/2. L1 and L2 Regularization.mp433.5MB
  235. 4. Regularization and Normalization/3. Dropout.mp475.22MB
  236. 4. Regularization and Normalization/4. DropConnect.mp414.18MB
  237. 4. Regularization and Normalization/5. Normalization.mp413.54MB
  238. 4. Regularization and Normalization/6. Batch Normalization.mp4100.34MB
  239. 4. Regularization and Normalization/7. Layer Normalization.mp445.48MB
  240. 4. Regularization and Normalization/8. Group Normalization.mp426.46MB
  241. 5. Optimization/1. Batch Gradient Descent.mp449.42MB
  242. 5. Optimization/10. SWATS - Switching from Adam to SGD.mp49.81MB
  243. 5. Optimization/11. Weight Decay.mp475.65MB
  244. 5. Optimization/12. Decoupling Weight Decay.mp452.25MB
  245. 5. Optimization/13. AMSGrad.mp485.64MB
  246. 5. Optimization/2. Stochastic Gradient Descent.mp418.11MB
  247. 5. Optimization/3. Mini-Batch Gradient Descent.mp46.94MB
  248. 5. Optimization/4. Exponentially Weighted Average Intuition.mp422.92MB
  249. 5. Optimization/5. Exponentially Weighted Average Implementation.mp443.15MB
  250. 5. Optimization/6. Bias Correction in Exponentially Weighted Averages.mp430.92MB
  251. 5. Optimization/7. Momentum.mp427.32MB
  252. 5. Optimization/8. RMSProp.mp438.96MB
  253. 5. Optimization/9. Adam Optimization.mp477.77MB
  254. 6. Hyperparameter Tuning and Learning Rate Scheduling/1. Introduction to Hyperparameter Tuning and Learning Rate Recap.mp417.65MB
  255. 6. Hyperparameter Tuning and Learning Rate Scheduling/2. Step Learning Rate Decay.mp462.86MB
  256. 6. Hyperparameter Tuning and Learning Rate Scheduling/3. Cyclic Learning Rate.mp469.37MB
  257. 6. Hyperparameter Tuning and Learning Rate Scheduling/4. Cosine Annealing with Warm Restarts.mp435.21MB
  258. 6. Hyperparameter Tuning and Learning Rate Scheduling/5. Batch Size vs Learning Rate.mp424.72MB
  259. 7. Weight Initialization/1. Normal Distribution.mp418.73MB
  260. 7. Weight Initialization/2. What happens when all weights are initialized to the same value.mp459.96MB
  261. 7. Weight Initialization/3. Xavier Initialization.mp4109.71MB
  262. 7. Weight Initialization/4. He Norm Initialization.mp413.32MB
  263. 8. Introduction to PyTorch/1. CODE FOR THIS COURSE.mp41.78MB
  264. 8. Introduction to PyTorch/10. Weight Initialization in PyTorch.mp465.88MB
  265. 8. Introduction to PyTorch/2. Computation Graphs and Deep Learning Frameworks.mp455.23MB
  266. 8. Introduction to PyTorch/3. Installing PyTorch and an Introduction.mp499.25MB
  267. 8. Introduction to PyTorch/4. How PyTorch Works.mp4147.44MB
  268. 8. Introduction to PyTorch/5. Torch Tensors - Part 1.mp487.09MB
  269. 8. Introduction to PyTorch/6. Torch Tensors - Part 2.mp467.94MB
  270. 8. Introduction to PyTorch/7. Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp475.07MB
  271. 8. Introduction to PyTorch/8. Automatic Differentiation.mp476.4MB
  272. 8. Introduction to PyTorch/9. Loss Functions in PyTorch.mp4222.75MB
  273. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/1. Part 1 Data Preprocessing.mp4123.77MB
  274. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/2. Part 2 Data Normalization.mp455.43MB
  275. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/3. Part 3 Creating and Loading the Dataset.mp466.2MB
  276. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network.mp4170.51MB
  277. 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network.mp4156.22MB
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