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[] Udemy - Machine Learning, Deep Learning and Bayesian Learning

  • 收录时间:2022-02-28 08:14:16
  • 文件大小:6GB
  • 下载次数:1
  • 最近下载:2022-02-28 08:14:16
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文件列表

  1. 03 - Machine Learning Numpy + Scikit Learn/012 CART part 2.mp4 166MB
  2. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/009 Semantic Segmentation training with PyTorch Lightning.mp4 130MB
  3. 04 - Machine Learning Classification + Time Series + Model Diagnostics/005 Titanic dataset.mp4 116MB
  4. 09 - Deep Learning Recurrent Neural Nets/010 Sequence to Sequence models Prediction step.mp4 105MB
  5. 13 - Deep Learning Transformers and BERT/008 Pytorch Lightning + DistilBERT for classification.mp4 103MB
  6. 05 - Unsupervised Learning/002 Fashion MNIST PCA.mp4 102MB
  7. 09 - Deep Learning Recurrent Neural Nets/005 Deep Learning - Long Short Term Memory (LSTM) Nets.mp4 91MB
  8. 03 - Machine Learning Numpy + Scikit Learn/009 Linear Regresson Part 1.mp4 91MB
  9. 04 - Machine Learning Classification + Time Series + Model Diagnostics/007 Sklearn classification.mp4 90MB
  10. 04 - Machine Learning Classification + Time Series + Model Diagnostics/019 Area Under Curve (AUC) Part 1.mp4 84MB
  11. 09 - Deep Learning Recurrent Neural Nets/008 Sequence to Sequence Introduction + Data Prep.mp4 80MB
  12. 10 - Deep Learning PyTorch Introduction/006 Deep Learning with Pytorch Stochastic Gradient Descent.mp4 79MB
  13. 03 - Machine Learning Numpy + Scikit Learn/004 Kmeans part 1.mp4 78MB
  14. 04 - Machine Learning Classification + Time Series + Model Diagnostics/012 FB Prophet part 1.mp4 78MB
  15. 06 - Natural Language Processing + Regularization/009 Feature Extraction with Spacy (using Pandas).mp4 76MB
  16. 09 - Deep Learning Recurrent Neural Nets/007 Transfer Learning - GLOVE vectors.mp4 75MB
  17. 03 - Machine Learning Numpy + Scikit Learn/010 Linear Regression Part 2.mp4 72MB
  18. 14 - Bayesian Learning and probabilistic programming/005 Coin Toss Example with Pymc3.mp4 71MB
  19. 14 - Bayesian Learning and probabilistic programming/004 Bayesian learning Population estimation pymc3 way.mp4 71MB
  20. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/008 Nose Tip detection with CNNs.mp4 69MB
  21. 03 - Machine Learning Numpy + Scikit Learn/015 Gradient Boosted Machines.mp4 68MB
  22. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/006 PyTorch Hooks Step through with breakpoints.mp4 68MB
  23. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/007 PyTorch Weighted CrossEntropy Loss.mp4 65MB
  24. 03 - Machine Learning Numpy + Scikit Learn/005 Kmeans part 2.mp4 63MB
  25. 02 - Basic python + Pandas + Plotting/005 Numpy functions.mp4 62MB
  26. 14 - Bayesian Learning and probabilistic programming/007 Bayesian Linear Regression with pymc3.mp4 60MB
  27. 07 - Deep Learning/009 Softmax theory.mp4 58MB
  28. 04 - Machine Learning Classification + Time Series + Model Diagnostics/018 Stratified K Fold.mp4 58MB
  29. 11 - Deep Learning Transfer Learning with PyTorch Lightning/009 Data Augmentation with Torchvision Transforms.mp4 57MB
  30. 07 - Deep Learning/007 MNIST and Softmax.mp4 56MB
  31. 14 - Bayesian Learning and probabilistic programming/009 Bayesian Rolling regression - pymc3 way.mp4 55MB
  32. 07 - Deep Learning/011 Batch Norm Theory.mp4 54MB
  33. 04 - Machine Learning Classification + Time Series + Model Diagnostics/017 Cross Validation.mp4 54MB
  34. 10 - Deep Learning PyTorch Introduction/005 Deep Learning with Pytorch Loss functions.mp4 52MB
  35. 10 - Deep Learning PyTorch Introduction/010 Deep Learning Intro to Pytorch Lightning.mp4 52MB
  36. 04 - Machine Learning Classification + Time Series + Model Diagnostics/008 Dealing with missing values.mp4 51MB
  37. 11 - Deep Learning Transfer Learning with PyTorch Lightning/006 PyTorch Lightning Trainer + Model evaluation.mp4 50MB
  38. 14 - Bayesian Learning and probabilistic programming/003 Bayes rule for population mean estimation.mp4 50MB
  39. 02 - Basic python + Pandas + Plotting/024 Seaborn + pair plots.mp4 50MB
  40. 01 - Introduction/002 How to tackle this course.mp4 49MB
  41. 13 - Deep Learning Transformers and BERT/007 Distilbert (Smaller BERT) model.mp4 49MB
  42. 05 - Unsupervised Learning/004 Other clustering methods.mp4 48MB
  43. 06 - Natural Language Processing + Regularization/016 Ridge regression (L2 penalised regression).mp4 47MB
  44. 04 - Machine Learning Classification + Time Series + Model Diagnostics/011 Loss functions.mp4 46MB
  45. 06 - Natural Language Processing + Regularization/005 NLTK + Stemming.mp4 46MB
  46. 02 - Basic python + Pandas + Plotting/020 Plot multiple lines.mp4 45MB
  47. 09 - Deep Learning Recurrent Neural Nets/003 Word2Vec keras Model API.mp4 45MB
  48. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/003 Keras Conv2D layer.mp4 44MB
  49. 14 - Bayesian Learning and probabilistic programming/012 Variational Bayes Linear Classification.mp4 44MB
  50. 09 - Deep Learning Recurrent Neural Nets/001 Word2vec and Embeddings.mp4 44MB
  51. 03 - Machine Learning Numpy + Scikit Learn/003 Gradient Descent.mp4 43MB
  52. 07 - Deep Learning/004 Tensorflow + Keras demo problem 1.mp4 43MB
  53. 01 - Introduction/003 Installations and sign ups.mp4 43MB
  54. 02 - Basic python + Pandas + Plotting/013 Pandas loc and iloc.mp4 42MB
  55. 01 - Introduction/001 Introduction.mp4 42MB
  56. 02 - Basic python + Pandas + Plotting/011 Pandas simple functions.mp4 38MB
  57. 03 - Machine Learning Numpy + Scikit Learn/014 Random Forest Code.mp4 37MB
  58. 14 - Bayesian Learning and probabilistic programming/002 Bayesian Learning Distributions.mp4 36MB
  59. 03 - Machine Learning Numpy + Scikit Learn/008 Intro.mp4 35MB
  60. 10 - Deep Learning PyTorch Introduction/003 Pytorch Dataset and DataLoaders.mp4 35MB
  61. 06 - Natural Language Processing + Regularization/004 Financial News Sentiment Classifier.mp4 34MB
  62. 10 - Deep Learning PyTorch Introduction/008 Pytorch Model API.mp4 33MB
  63. 06 - Natural Language Processing + Regularization/008 Spacy intro.mp4 33MB
  64. 06 - Natural Language Processing + Regularization/011 Over-sampling.mp4 33MB
  65. 07 - Deep Learning/006 First example with Relu.mp4 33MB
  66. 02 - Basic python + Pandas + Plotting/015 Pandas map and apply.mp4 31MB
  67. 06 - Natural Language Processing + Regularization/018 L1 Penalised Regression (Lasso).mp4 31MB
  68. 09 - Deep Learning Recurrent Neural Nets/009 Sequence to Sequence model + Keras Model API.mp4 30MB
  69. 14 - Bayesian Learning and probabilistic programming/010 Bayesian Rolling Regression - forecasting.mp4 30MB
  70. 15 - Model Deployment/004 FastAPI serving model.mp4 29MB
  71. 13 - Deep Learning Transformers and BERT/006 Tokenizers and data prep for BERT models.mp4 29MB
  72. 13 - Deep Learning Transformers and BERT/003 Encoder Transformer Models The Maths.mp4 29MB
  73. 09 - Deep Learning Recurrent Neural Nets/002 Kaggle + Word2Vec.mp4 28MB
  74. 02 - Basic python + Pandas + Plotting/004 Python functions (methods).mp4 28MB
  75. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/007 Cifar-10.mp4 27MB
  76. 04 - Machine Learning Classification + Time Series + Model Diagnostics/004 Theory part 2 + code.mp4 27MB
  77. 03 - Machine Learning Numpy + Scikit Learn/006 Broadcasting.mp4 27MB
  78. 01 - Introduction/30889860-course-code-material.zip 26MB
  79. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/001 Introduction.mp4 25MB
  80. 06 - Natural Language Processing + Regularization/017 S&P500 data preparation for L1 loss.mp4 25MB
  81. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/005 PyTorch Hooks.mp4 25MB
  82. 04 - Machine Learning Classification + Time Series + Model Diagnostics/013 FB Prophet part 2.mp4 24MB
  83. 06 - Natural Language Processing + Regularization/010 Classification Example.mp4 24MB
  84. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/005 Dropout theory and code.mp4 24MB
  85. 13 - Deep Learning Transformers and BERT/002 The illustrated Transformer (blogpost by Jay Alammar).mp4 24MB
  86. 06 - Natural Language Processing + Regularization/019 L1 L2 Penalty theory why it works.mp4 23MB
  87. 07 - Deep Learning/003 DL theory part 2.mp4 23MB
  88. 05 - Unsupervised Learning/003 K-means.mp4 22MB
  89. 02 - Basic python + Pandas + Plotting/012 Pandas Subsetting.mp4 22MB
  90. 02 - Basic python + Pandas + Plotting/002 Basic Data Structures.mp4 22MB
  91. 02 - Basic python + Pandas + Plotting/021 Histograms.mp4 22MB
  92. 11 - Deep Learning Transfer Learning with PyTorch Lightning/015 WandB for logging experiments.mp4 22MB
  93. 15 - Model Deployment/006 Streamlit functions.mp4 21MB
  94. 05 - Unsupervised Learning/001 Principal Component Analysis (PCA) theory.mp4 21MB
  95. 05 - Unsupervised Learning/006 Gaussian Mixture Models (GMM) theory.mp4 20MB
  96. 03 - Machine Learning Numpy + Scikit Learn/011 Classification and Regression Trees.mp4 20MB
  97. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/002 Fashion MNIST feed forward net for benchmarking.mp4 20MB
  98. 04 - Machine Learning Classification + Time Series + Model Diagnostics/020 Area Under Curve (AUC) Part 2.mp4 19MB
  99. 04 - Machine Learning Classification + Time Series + Model Diagnostics/016 Overfitting.mp4 19MB
  100. 09 - Deep Learning Recurrent Neural Nets/004 Recurrent Neural Nets - Theory.mp4 19MB
  101. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/002 Coco Dataset + Augmentations for Segmentation with Torchvision.mp4 19MB
  102. 02 - Basic python + Pandas + Plotting/003 Dictionaries.mp4 19MB
  103. 15 - Model Deployment/007 CLIP model.mp4 19MB
  104. 02 - Basic python + Pandas + Plotting/022 Scatter Plots.mp4 19MB
  105. 02 - Basic python + Pandas + Plotting/016 Pandas groupby.mp4 18MB
  106. 06 - Natural Language Processing + Regularization/014 MSE recap.mp4 18MB
  107. 14 - Bayesian Learning and probabilistic programming/001 Introduction and Terminology.mp4 18MB
  108. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/006 MaxPool (and comparison to stride).mp4 18MB
  109. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/004 Model fitting and discussion of results.mp4 17MB
  110. 07 - Deep Learning/002 DL theory part 1.mp4 17MB
  111. 14 - Bayesian Learning and probabilistic programming/006 Data Setup for Bayesian Linear Regression.mp4 17MB
  112. 07 - Deep Learning/010 Batch Norm.mp4 17MB
  113. 04 - Machine Learning Classification + Time Series + Model Diagnostics/014 Theory behind FB Prophet.mp4 17MB
  114. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/008 Weights and Biases Logging images.mp4 16MB
  115. 11 - Deep Learning Transfer Learning with PyTorch Lightning/004 PyTorch transfer learning with ResNet.mp4 15MB
  116. 07 - Deep Learning/005 Activation functions.mp4 15MB
  117. 02 - Basic python + Pandas + Plotting/023 Subplots.mp4 15MB
  118. 11 - Deep Learning Transfer Learning with PyTorch Lightning/008 Cassava Leaf Dataset.mp4 15MB
  119. 14 - Bayesian Learning and probabilistic programming/008 Bayesian Rolling Regression - Problem setup.mp4 15MB
  120. 11 - Deep Learning Transfer Learning with PyTorch Lightning/003 PyTorch datasets + Torchvision.mp4 15MB
  121. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/003 Unet Architecture overview.mp4 15MB
  122. 04 - Machine Learning Classification + Time Series + Model Diagnostics/006 Sklearn classification prelude.mp4 14MB
  123. 02 - Basic python + Pandas + Plotting/014 Pandas loc and iloc 2.mp4 14MB
  124. 06 - Natural Language Processing + Regularization/006 N-grams.mp4 14MB
  125. 16 - Final Thoughts/001 Some advice on your journey.mp4 14MB
  126. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/004 PyTorch Model Architecture.mp4 14MB
  127. 04 - Machine Learning Classification + Time Series + Model Diagnostics/003 Theory part 1.mp4 14MB
  128. 05 - Unsupervised Learning/005 DBSCAN theory.mp4 13MB
  129. 02 - Basic python + Pandas + Plotting/006 Conditional statements.mp4 13MB
  130. 06 - Natural Language Processing + Regularization/007 Word (feature) importance.mp4 12MB
  131. 10 - Deep Learning PyTorch Introduction/002 Pytorch TensorDataset.mp4 12MB
  132. 02 - Basic python + Pandas + Plotting/007 For loops.mp4 12MB
  133. 15 - Model Deployment/003 FastAPI intro.mp4 12MB
  134. 04 - Machine Learning Classification + Time Series + Model Diagnostics/010 Intro.mp4 11MB
  135. 04 - Machine Learning Classification + Time Series + Model Diagnostics/002 Kaggle part 2.mp4 11MB
  136. 14 - Bayesian Learning and probabilistic programming/014 Minibatch Variational Bayes.mp4 11MB
  137. 10 - Deep Learning PyTorch Introduction/004 Deep Learning with PyTorch nn.Sequential models.mp4 11MB
  138. 06 - Natural Language Processing + Regularization/002 Stop words and Term Frequency.mp4 11MB
  139. 14 - Bayesian Learning and probabilistic programming/016 Deep Bayesian Networks - analysis.mp4 10MB
  140. 06 - Natural Language Processing + Regularization/001 Intro.mp4 10MB
  141. 07 - Deep Learning/008 Deep Learning Input Normalisation.mp4 10MB
  142. 10 - Deep Learning PyTorch Introduction/007 Deep Learning with Pytorch Optimizers.mp4 10MB
  143. 06 - Natural Language Processing + Regularization/015 L2 Loss Ridge Regression intro.mp4 10MB
  144. 11 - Deep Learning Transfer Learning with PyTorch Lightning/005 PyTorch Lightning Model.mp4 9MB
  145. 11 - Deep Learning Transfer Learning with PyTorch Lightning/002 Kaggle problem description.mp4 9MB
  146. 01 - Introduction/004 Jupyter Notebooks.mp4 9MB
  147. 14 - Bayesian Learning and probabilistic programming/011 Variational Bayes Intro.mp4 9MB
  148. 02 - Basic python + Pandas + Plotting/019 Line plot.mp4 9MB
  149. 11 - Deep Learning Transfer Learning with PyTorch Lightning/013 Cross Entropy Loss for Imbalanced Classes.mp4 8MB
  150. 11 - Deep Learning Transfer Learning with PyTorch Lightning/012 Setting up PyTorch Lightning for training.mp4 8MB
  151. 06 - Natural Language Processing + Regularization/013 Introduction.mp4 8MB
  152. 13 - Deep Learning Transformers and BERT/004 BERT - The theory.mp4 8MB
  153. 11 - Deep Learning Transfer Learning with PyTorch Lightning/011 Deep Learning Transfer Learning Model with ResNet.mp4 8MB
  154. 11 - Deep Learning Transfer Learning with PyTorch Lightning/010 Train vs Test Augmentations + DataLoader parameters.mp4 8MB
  155. 15 - Model Deployment/002 Saving Models.mp4 8MB
  156. 14 - Bayesian Learning and probabilistic programming/013 Variational Bayesian Inference Result Analysis.mp4 7MB
  157. 14 - Bayesian Learning and probabilistic programming/015 Deep Bayesian Networks.mp4 7MB
  158. 11 - Deep Learning Transfer Learning with PyTorch Lightning/014 PyTorch Test dataset setup and evaluation.mp4 7MB
  159. 13 - Deep Learning Transformers and BERT/005 Kaggle Multi-lingual Toxic Comment Classification Challenge.mp4 7MB
  160. 04 - Machine Learning Classification + Time Series + Model Diagnostics/001 Kaggle part 1.mp4 7MB
  161. 02 - Basic python + Pandas + Plotting/008 Dictionaries again.mp4 6MB
  162. 06 - Natural Language Processing + Regularization/003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory.mp4 6MB
  163. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/001 Intro.mp4 6MB
  164. 15 - Model Deployment/005 Streamlit Intro.mp4 6MB
  165. 09 - Deep Learning Recurrent Neural Nets/006 Deep Learning - Stacking LSTMs + GRUs.mp4 5MB
  166. 02 - Basic python + Pandas + Plotting/010 Intro.mp4 5MB
  167. 10 - Deep Learning PyTorch Introduction/009 Pytorch in GPUs.mp4 5MB
  168. 03 - Machine Learning Numpy + Scikit Learn/013 Random Forest theory.mp4 5MB
  169. 11 - Deep Learning Transfer Learning with PyTorch Lightning/001 Transfer Learning Introduction.mp4 4MB
  170. 11 - Deep Learning Transfer Learning with PyTorch Lightning/007 Deep Learning for Cassava Leaf Classification.mp4 4MB
  171. 13 - Deep Learning Transformers and BERT/001 Introduction to Transformers.mp4 3MB
  172. 02 - Basic python + Pandas + Plotting/001 Intro.mp4 3MB
  173. 02 - Basic python + Pandas + Plotting/31237618-03-0-plotting.zip 3MB
  174. 07 - Deep Learning/32725408-09-tensorflow.zip 3MB
  175. 06 - Natural Language Processing + Regularization/31762302-06-0-reguralisation.zip 3MB
  176. 15 - Model Deployment/001 Intro.mp4 3MB
  177. 10 - Deep Learning PyTorch Introduction/001 Introduction.mp4 2MB
  178. 03 - Machine Learning Numpy + Scikit Learn/001 Your reviews are important to me!.mp4 2MB
  179. 14 - Bayesian Learning and probabilistic programming/31919076-bayesian-inference.zip 2MB
  180. 07 - Deep Learning/001 Intro.mp4 633KB
  181. 02 - Basic python + Pandas + Plotting/34142844-04-pairplots.ipynb 200KB
  182. 03 - Machine Learning Numpy + Scikit Learn/012 CART part 2_en.vtt 21KB
  183. 03 - Machine Learning Numpy + Scikit Learn/005 Kmeans part 2_en.vtt 20KB
  184. 13 - Deep Learning Transformers and BERT/008 Pytorch Lightning + DistilBERT for classification_en.vtt 17KB
  185. 03 - Machine Learning Numpy + Scikit Learn/003 Gradient Descent_en.vtt 17KB
  186. 07 - Deep Learning/004 Tensorflow + Keras demo problem 1_en.vtt 16KB
  187. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/009 Semantic Segmentation training with PyTorch Lightning_en.vtt 16KB
  188. 04 - Machine Learning Classification + Time Series + Model Diagnostics/005 Titanic dataset_en.vtt 15KB
  189. 04 - Machine Learning Classification + Time Series + Model Diagnostics/007 Sklearn classification_en.vtt 14KB
  190. 09 - Deep Learning Recurrent Neural Nets/003 Word2Vec keras Model API_en.vtt 13KB
  191. 09 - Deep Learning Recurrent Neural Nets/010 Sequence to Sequence models Prediction step_en.vtt 13KB
  192. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/008 Nose Tip detection with CNNs_en.vtt 12KB
  193. 03 - Machine Learning Numpy + Scikit Learn/009 Linear Regresson Part 1_en.vtt 12KB
  194. 03 - Machine Learning Numpy + Scikit Learn/004 Kmeans part 1_en.vtt 12KB
  195. 09 - Deep Learning Recurrent Neural Nets/005 Deep Learning - Long Short Term Memory (LSTM) Nets_en.vtt 12KB
  196. 09 - Deep Learning Recurrent Neural Nets/007 Transfer Learning - GLOVE vectors_en.vtt 11KB
  197. 02 - Basic python + Pandas + Plotting/011 Pandas simple functions_en.vtt 11KB
  198. 03 - Machine Learning Numpy + Scikit Learn/010 Linear Regression Part 2_en.vtt 11KB
  199. 13 - Deep Learning Transformers and BERT/006 Tokenizers and data prep for BERT models_en.vtt 11KB
  200. 13 - Deep Learning Transformers and BERT/007 Distilbert (Smaller BERT) model_en.vtt 11KB
  201. 02 - Basic python + Pandas + Plotting/005 Numpy functions_en.vtt 11KB
  202. 09 - Deep Learning Recurrent Neural Nets/004 Recurrent Neural Nets - Theory_en.vtt 11KB
  203. 09 - Deep Learning Recurrent Neural Nets/002 Kaggle + Word2Vec_en.vtt 11KB
  204. 05 - Unsupervised Learning/002 Fashion MNIST PCA_en.vtt 10KB
  205. 14 - Bayesian Learning and probabilistic programming/002 Bayesian Learning Distributions_en.vtt 10KB
  206. 07 - Deep Learning/007 MNIST and Softmax_en.vtt 10KB
  207. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/007 Cifar-10_en.vtt 10KB
  208. 06 - Natural Language Processing + Regularization/004 Financial News Sentiment Classifier_en.vtt 10KB
  209. 14 - Bayesian Learning and probabilistic programming/007 Bayesian Linear Regression with pymc3_en.vtt 10KB
  210. 04 - Machine Learning Classification + Time Series + Model Diagnostics/018 Stratified K Fold_en.vtt 10KB
  211. 06 - Natural Language Processing + Regularization/009 Feature Extraction with Spacy (using Pandas)_en.vtt 10KB
  212. 04 - Machine Learning Classification + Time Series + Model Diagnostics/012 FB Prophet part 1_en.vtt 10KB
  213. 03 - Machine Learning Numpy + Scikit Learn/015 Gradient Boosted Machines_en.vtt 10KB
  214. 03 - Machine Learning Numpy + Scikit Learn/006 Broadcasting_en.vtt 10KB
  215. 10 - Deep Learning PyTorch Introduction/010 Deep Learning Intro to Pytorch Lightning_en.vtt 9KB
  216. 14 - Bayesian Learning and probabilistic programming/009 Bayesian Rolling regression - pymc3 way_en.vtt 9KB
  217. 04 - Machine Learning Classification + Time Series + Model Diagnostics/019 Area Under Curve (AUC) Part 1_en.vtt 9KB
  218. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/007 PyTorch Weighted CrossEntropy Loss_en.vtt 9KB
  219. 05 - Unsupervised Learning/001 Principal Component Analysis (PCA) theory_en.vtt 9KB
  220. 14 - Bayesian Learning and probabilistic programming/003 Bayes rule for population mean estimation_en.vtt 9KB
  221. 13 - Deep Learning Transformers and BERT/002 The illustrated Transformer (blogpost by Jay Alammar)_en.vtt 9KB
  222. 14 - Bayesian Learning and probabilistic programming/004 Bayesian learning Population estimation pymc3 way_en.vtt 9KB
  223. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/006 PyTorch Hooks Step through with breakpoints_en.vtt 9KB
  224. 09 - Deep Learning Recurrent Neural Nets/009 Sequence to Sequence model + Keras Model API_en.vtt 9KB
  225. 10 - Deep Learning PyTorch Introduction/005 Deep Learning with Pytorch Loss functions_en.vtt 9KB
  226. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/003 Keras Conv2D layer_en.vtt 9KB
  227. 14 - Bayesian Learning and probabilistic programming/001 Introduction and Terminology_en.vtt 8KB
  228. 09 - Deep Learning Recurrent Neural Nets/001 Word2vec and Embeddings_en.vtt 8KB
  229. 07 - Deep Learning/011 Batch Norm Theory_en.vtt 8KB
  230. 04 - Machine Learning Classification + Time Series + Model Diagnostics/017 Cross Validation_en.vtt 8KB
  231. 02 - Basic python + Pandas + Plotting/015 Pandas map and apply_en.vtt 8KB
  232. 10 - Deep Learning PyTorch Introduction/006 Deep Learning with Pytorch Stochastic Gradient Descent_en.vtt 8KB
  233. 14 - Bayesian Learning and probabilistic programming/005 Coin Toss Example with Pymc3_en.vtt 8KB
  234. 09 - Deep Learning Recurrent Neural Nets/008 Sequence to Sequence Introduction + Data Prep_en.vtt 8KB
  235. 02 - Basic python + Pandas + Plotting/024 Seaborn + pair plots_en.vtt 8KB
  236. 06 - Natural Language Processing + Regularization/016 Ridge regression (L2 penalised regression)_en.vtt 8KB
  237. 05 - Unsupervised Learning/006 Gaussian Mixture Models (GMM) theory_en.vtt 8KB
  238. 02 - Basic python + Pandas + Plotting/021 Histograms_en.vtt 8KB
  239. 06 - Natural Language Processing + Regularization/005 NLTK + Stemming_en.vtt 8KB
  240. 02 - Basic python + Pandas + Plotting/013 Pandas loc and iloc_en.vtt 8KB
  241. 05 - Unsupervised Learning/003 K-means_en.vtt 8KB
  242. 15 - Model Deployment/004 FastAPI serving model_en.vtt 8KB
  243. 14 - Bayesian Learning and probabilistic programming/012 Variational Bayes Linear Classification_en.vtt 8KB
  244. 15 - Model Deployment/007 CLIP model_en.vtt 7KB
  245. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/005 PyTorch Hooks_en.vtt 7KB
  246. 05 - Unsupervised Learning/004 Other clustering methods_en.vtt 7KB
  247. 04 - Machine Learning Classification + Time Series + Model Diagnostics/011 Loss functions_en.vtt 7KB
  248. 06 - Natural Language Processing + Regularization/017 S&P500 data preparation for L1 loss_en.vtt 7KB
  249. 02 - Basic python + Pandas + Plotting/016 Pandas groupby_en.vtt 7KB
  250. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/005 Dropout theory and code_en.vtt 7KB
  251. 04 - Machine Learning Classification + Time Series + Model Diagnostics/016 Overfitting_en.vtt 7KB
  252. 04 - Machine Learning Classification + Time Series + Model Diagnostics/020 Area Under Curve (AUC) Part 2_en.vtt 7KB
  253. 05 - Unsupervised Learning/005 DBSCAN theory_en.vtt 7KB
  254. 04 - Machine Learning Classification + Time Series + Model Diagnostics/003 Theory part 1_en.vtt 7KB
  255. 03 - Machine Learning Numpy + Scikit Learn/014 Random Forest Code_en.vtt 7KB
  256. 03 - Machine Learning Numpy + Scikit Learn/011 Classification and Regression Trees_en.vtt 6KB
  257. 02 - Basic python + Pandas + Plotting/002 Basic Data Structures_en.vtt 6KB
  258. 02 - Basic python + Pandas + Plotting/022 Scatter Plots_en.vtt 6KB
  259. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/003 Unet Architecture overview_en.vtt 6KB
  260. 11 - Deep Learning Transfer Learning with PyTorch Lightning/006 PyTorch Lightning Trainer + Model evaluation_en.vtt 6KB
  261. 04 - Machine Learning Classification + Time Series + Model Diagnostics/004 Theory part 2 + code_en.vtt 6KB
  262. 02 - Basic python + Pandas + Plotting/012 Pandas Subsetting_en.vtt 6KB
  263. 01 - Introduction/002 How to tackle this course_en.vtt 6KB
  264. 07 - Deep Learning/002 DL theory part 1_en.vtt 6KB
  265. 06 - Natural Language Processing + Regularization/014 MSE recap_en.vtt 6KB
  266. 15 - Model Deployment/006 Streamlit functions_en.vtt 6KB
  267. 02 - Basic python + Pandas + Plotting/023 Subplots_en.vtt 6KB
  268. 12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/002 Coco Dataset + Augmentations for Segmentation with Torchvision_en.vtt 6KB
  269. 04 - Machine Learning Classification + Time Series + Model Diagnostics/010 Intro_en.vtt 6KB
  270. 11 - Deep Learning Transfer Learning with PyTorch Lightning/009 Data Augmentation with Torchvision Transforms_en.vtt 6KB
  271. 04 - Machine Learning Classification + Time Series + Model Diagnostics/014 Theory behind FB Prophet_en.vtt 6KB
  272. 06 - Natural Language Processing + Regularization/011 Over-sampling_en.vtt 6KB
  273. 04 - Machine Learning Classification + Time Series + Model Diagnostics/008 Dealing with missing values_en.vtt 6KB
  274. 10 - Deep Learning PyTorch Introduction/003 Pytorch Dataset and DataLoaders_en.vtt 6KB
  275. 10 - Deep Learning PyTorch Introduction/004 Deep Learning with PyTorch nn.Sequential models_en.vtt 6KB
  276. 07 - Deep Learning/010 Batch Norm_en.vtt 6KB
  277. 06 - Natural Language Processing + Regularization/018 L1 Penalised Regression (Lasso)_en.vtt 6KB
  278. 14 - Bayesian Learning and probabilistic programming/008 Bayesian Rolling Regression - Problem setup_en.vtt 6KB
  279. 13 - Deep Learning Transformers and BERT/003 Encoder Transformer Models The Maths_en.vtt 6KB
  280. 06 - Natural Language Processing + Regularization/008 Spacy intro_en.vtt 6KB
  281. 02 - Basic python + Pandas + Plotting/004 Python functions (methods)_en.vtt 6KB
  282. 07 - Deep Learning/009 Softmax theory_en.vtt 6KB
  283. 07 - Deep Learning/005 Activation functions_en.vtt 6KB
  284. 10 - Deep Learning PyTorch Introduction/008 Pytorch Model API_en.vtt 6KB
  285. 07 - Deep Learning/006 First example with Relu_en.vtt 5KB
  286. 11 - Deep Learning Transfer Learning with PyTorch Lightning/015 WandB for logging experiments_en.vtt 5KB
  287. 08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/006 MaxPool (and comparison to stride)_en.vtt 5KB
  288. 06 - Natural Language Processing + Regularization/001 Intro_en.vtt 5KB
  289. 14 - Bayesian Learning and probabilistic programming/010 Bayesian Rolling Regression - forecasting_en.vtt 5KB
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