Udemy - Machine Learning, Deep Learning and Bayesian Learning

Creation Time	June 21, 2024, 10:16 p.m.
Last Access Time	Nov. 19, 2024, 12:21 p.m.
File Size	5.5 GB
Keywords	and - Deep Udemy Machine Learning Bayesian
Total Requests	58
Total Files	368
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03 - Machine Learning Numpy + Scikit Learn/012 CART part 2.mp4 166.5 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/009 Semantic Segmentation training with PyTorch Lightning.mp4 130.2 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/005 Titanic dataset.mp4 116.3 MB
09 - Deep Learning Recurrent Neural Nets/010 Sequence to Sequence models Prediction step.mp4 104.7 MB
13 - Deep Learning Transformers and BERT/008 Pytorch Lightning + DistilBERT for classification.mp4 102.7 MB
05 - Unsupervised Learning/002 Fashion MNIST PCA.mp4 102.1 MB
09 - Deep Learning Recurrent Neural Nets/005 Deep Learning - Long Short Term Memory (LSTM) Nets.mp4 91.0 MB
03 - Machine Learning Numpy + Scikit Learn/009 Linear Regresson Part 1.mp4 90.5 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/007 Sklearn classification.mp4 90.0 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/019 Area Under Curve (AUC) Part 1.mp4 84.1 MB
09 - Deep Learning Recurrent Neural Nets/008 Sequence to Sequence Introduction + Data Prep.mp4 80.1 MB
10 - Deep Learning PyTorch Introduction/006 Deep Learning with Pytorch Stochastic Gradient Descent.mp4 79.5 MB
03 - Machine Learning Numpy + Scikit Learn/004 Kmeans part 1.mp4 78.4 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/012 FB Prophet part 1.mp4 78.0 MB
06 - Natural Language Processing + Regularization/009 Feature Extraction with Spacy (using Pandas).mp4 76.5 MB
09 - Deep Learning Recurrent Neural Nets/007 Transfer Learning - GLOVE vectors.mp4 74.6 MB
03 - Machine Learning Numpy + Scikit Learn/010 Linear Regression Part 2.mp4 71.6 MB
14 - Bayesian Learning and probabilistic programming/005 Coin Toss Example with Pymc3.mp4 70.7 MB
14 - Bayesian Learning and probabilistic programming/004 Bayesian learning Population estimation pymc3 way.mp4 70.6 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/008 Nose Tip detection with CNNs.mp4 68.7 MB
03 - Machine Learning Numpy + Scikit Learn/015 Gradient Boosted Machines.mp4 67.6 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/006 PyTorch Hooks Step through with breakpoints.mp4 67.6 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/007 PyTorch Weighted CrossEntropy Loss.mp4 65.2 MB
03 - Machine Learning Numpy + Scikit Learn/005 Kmeans part 2.mp4 63.2 MB
02 - Basic python + Pandas + Plotting/005 Numpy functions.mp4 62.4 MB
14 - Bayesian Learning and probabilistic programming/007 Bayesian Linear Regression with pymc3.mp4 60.1 MB
07 - Deep Learning/009 Softmax theory.mp4 58.3 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/018 Stratified K Fold.mp4 58.1 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/009 Data Augmentation with Torchvision Transforms.mp4 56.5 MB
07 - Deep Learning/007 MNIST and Softmax.mp4 55.8 MB
14 - Bayesian Learning and probabilistic programming/009 Bayesian Rolling regression - pymc3 way.mp4 54.8 MB
07 - Deep Learning/011 Batch Norm Theory.mp4 53.9 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/017 Cross Validation.mp4 53.7 MB
10 - Deep Learning PyTorch Introduction/005 Deep Learning with Pytorch Loss functions.mp4 52.4 MB
10 - Deep Learning PyTorch Introduction/010 Deep Learning Intro to Pytorch Lightning.mp4 52.4 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/008 Dealing with missing values.mp4 50.8 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/006 PyTorch Lightning Trainer + Model evaluation.mp4 50.2 MB
14 - Bayesian Learning and probabilistic programming/003 Bayes rule for population mean estimation.mp4 50.2 MB
02 - Basic python + Pandas + Plotting/024 Seaborn + pair plots.mp4 49.7 MB
01 - Introduction/002 How to tackle this course.mp4 48.9 MB
13 - Deep Learning Transformers and BERT/007 Distilbert (Smaller BERT) model.mp4 48.8 MB
05 - Unsupervised Learning/004 Other clustering methods.mp4 48.1 MB
06 - Natural Language Processing + Regularization/016 Ridge regression (L2 penalised regression).mp4 47.0 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/011 Loss functions.mp4 46.4 MB
06 - Natural Language Processing + Regularization/005 NLTK + Stemming.mp4 45.6 MB
02 - Basic python + Pandas + Plotting/020 Plot multiple lines.mp4 45.4 MB
09 - Deep Learning Recurrent Neural Nets/003 Word2Vec keras Model API.mp4 45.2 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/003 Keras Conv2D layer.mp4 44.5 MB
14 - Bayesian Learning and probabilistic programming/012 Variational Bayes Linear Classification.mp4 44.3 MB
09 - Deep Learning Recurrent Neural Nets/001 Word2vec and Embeddings.mp4 44.0 MB
03 - Machine Learning Numpy + Scikit Learn/003 Gradient Descent.mp4 43.4 MB
07 - Deep Learning/004 Tensorflow + Keras demo problem 1.mp4 43.3 MB
01 - Introduction/003 Installations and sign ups.mp4 42.8 MB
02 - Basic python + Pandas + Plotting/013 Pandas loc and iloc.mp4 41.8 MB
01 - Introduction/001 Introduction.mp4 41.8 MB
02 - Basic python + Pandas + Plotting/011 Pandas simple functions.mp4 38.3 MB
03 - Machine Learning Numpy + Scikit Learn/014 Random Forest Code.mp4 36.7 MB
14 - Bayesian Learning and probabilistic programming/002 Bayesian Learning Distributions.mp4 35.9 MB
03 - Machine Learning Numpy + Scikit Learn/008 Intro.mp4 35.4 MB
10 - Deep Learning PyTorch Introduction/003 Pytorch Dataset and DataLoaders.mp4 35.4 MB
06 - Natural Language Processing + Regularization/004 Financial News Sentiment Classifier.mp4 33.7 MB
10 - Deep Learning PyTorch Introduction/008 Pytorch Model API.mp4 33.2 MB
06 - Natural Language Processing + Regularization/008 Spacy intro.mp4 33.2 MB
06 - Natural Language Processing + Regularization/011 Over-sampling.mp4 32.8 MB
07 - Deep Learning/006 First example with Relu.mp4 32.6 MB
02 - Basic python + Pandas + Plotting/015 Pandas map and apply.mp4 31.4 MB
06 - Natural Language Processing + Regularization/018 L1 Penalised Regression (Lasso).mp4 31.4 MB
09 - Deep Learning Recurrent Neural Nets/009 Sequence to Sequence model + Keras Model API.mp4 30.5 MB
14 - Bayesian Learning and probabilistic programming/010 Bayesian Rolling Regression - forecasting.mp4 30.3 MB
15 - Model Deployment/004 FastAPI serving model.mp4 29.3 MB
13 - Deep Learning Transformers and BERT/006 Tokenizers and data prep for BERT models.mp4 29.1 MB
13 - Deep Learning Transformers and BERT/003 Encoder Transformer Models The Maths.mp4 28.7 MB
09 - Deep Learning Recurrent Neural Nets/002 Kaggle + Word2Vec.mp4 27.8 MB
02 - Basic python + Pandas + Plotting/004 Python functions (methods).mp4 27.6 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/007 Cifar-10.mp4 27.3 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/004 Theory part 2 + code.mp4 27.3 MB
03 - Machine Learning Numpy + Scikit Learn/006 Broadcasting.mp4 27.1 MB
01 - Introduction/30889860-course-code-material.zip 26.2 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/001 Introduction.mp4 25.3 MB
06 - Natural Language Processing + Regularization/017 S&P500 data preparation for L1 loss.mp4 25.2 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/005 PyTorch Hooks.mp4 24.7 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/013 FB Prophet part 2.mp4 24.5 MB
06 - Natural Language Processing + Regularization/010 Classification Example.mp4 24.1 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/005 Dropout theory and code.mp4 23.7 MB
13 - Deep Learning Transformers and BERT/002 The illustrated Transformer (blogpost by Jay Alammar).mp4 23.6 MB
06 - Natural Language Processing + Regularization/019 L1 L2 Penalty theory why it works.mp4 23.2 MB
07 - Deep Learning/003 DL theory part 2.mp4 22.8 MB
05 - Unsupervised Learning/003 K-means.mp4 22.3 MB
02 - Basic python + Pandas + Plotting/012 Pandas Subsetting.mp4 22.0 MB
02 - Basic python + Pandas + Plotting/002 Basic Data Structures.mp4 21.9 MB
02 - Basic python + Pandas + Plotting/021 Histograms.mp4 21.6 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/015 WandB for logging experiments.mp4 21.5 MB
15 - Model Deployment/006 Streamlit functions.mp4 20.8 MB
05 - Unsupervised Learning/001 Principal Component Analysis (PCA) theory.mp4 20.5 MB
05 - Unsupervised Learning/006 Gaussian Mixture Models (GMM) theory.mp4 20.0 MB
03 - Machine Learning Numpy + Scikit Learn/011 Classification and Regression Trees.mp4 20.0 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/002 Fashion MNIST feed forward net for benchmarking.mp4 19.7 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/020 Area Under Curve (AUC) Part 2.mp4 19.5 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/016 Overfitting.mp4 19.3 MB
09 - Deep Learning Recurrent Neural Nets/004 Recurrent Neural Nets - Theory.mp4 19.1 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/002 Coco Dataset + Augmentations for Segmentation with Torchvision.mp4 18.9 MB
02 - Basic python + Pandas + Plotting/003 Dictionaries.mp4 18.8 MB
15 - Model Deployment/007 CLIP model.mp4 18.7 MB
02 - Basic python + Pandas + Plotting/022 Scatter Plots.mp4 18.6 MB
02 - Basic python + Pandas + Plotting/016 Pandas groupby.mp4 18.3 MB
06 - Natural Language Processing + Regularization/014 MSE recap.mp4 18.3 MB
14 - Bayesian Learning and probabilistic programming/001 Introduction and Terminology.mp4 18.1 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/006 MaxPool (and comparison to stride).mp4 17.7 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/004 Model fitting and discussion of results.mp4 17.4 MB
07 - Deep Learning/002 DL theory part 1.mp4 17.2 MB
14 - Bayesian Learning and probabilistic programming/006 Data Setup for Bayesian Linear Regression.mp4 17.1 MB
07 - Deep Learning/010 Batch Norm.mp4 17.0 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/014 Theory behind FB Prophet.mp4 16.9 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/008 Weights and Biases Logging images.mp4 15.8 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/004 PyTorch transfer learning with ResNet.mp4 15.4 MB
07 - Deep Learning/005 Activation functions.mp4 15.4 MB
02 - Basic python + Pandas + Plotting/023 Subplots.mp4 15.3 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/008 Cassava Leaf Dataset.mp4 15.3 MB
14 - Bayesian Learning and probabilistic programming/008 Bayesian Rolling Regression - Problem setup.mp4 14.8 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/003 PyTorch datasets + Torchvision.mp4 14.7 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/003 Unet Architecture overview.mp4 14.7 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/006 Sklearn classification prelude.mp4 14.3 MB
02 - Basic python + Pandas + Plotting/014 Pandas loc and iloc 2.mp4 13.8 MB
06 - Natural Language Processing + Regularization/006 N-grams.mp4 13.8 MB
16 - Final Thoughts/001 Some advice on your journey.mp4 13.6 MB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/004 PyTorch Model Architecture.mp4 13.5 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/003 Theory part 1.mp4 13.5 MB
05 - Unsupervised Learning/005 DBSCAN theory.mp4 13.2 MB
02 - Basic python + Pandas + Plotting/006 Conditional statements.mp4 12.6 MB
06 - Natural Language Processing + Regularization/007 Word (feature) importance.mp4 12.4 MB
10 - Deep Learning PyTorch Introduction/002 Pytorch TensorDataset.mp4 12.4 MB
02 - Basic python + Pandas + Plotting/007 For loops.mp4 12.4 MB
15 - Model Deployment/003 FastAPI intro.mp4 11.6 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/010 Intro.mp4 11.4 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/002 Kaggle part 2.mp4 11.1 MB
14 - Bayesian Learning and probabilistic programming/014 Minibatch Variational Bayes.mp4 11.0 MB
10 - Deep Learning PyTorch Introduction/004 Deep Learning with PyTorch nn.Sequential models.mp4 11.0 MB
06 - Natural Language Processing + Regularization/002 Stop words and Term Frequency.mp4 10.7 MB
14 - Bayesian Learning and probabilistic programming/016 Deep Bayesian Networks - analysis.mp4 10.5 MB
06 - Natural Language Processing + Regularization/001 Intro.mp4 10.4 MB
07 - Deep Learning/008 Deep Learning Input Normalisation.mp4 10.3 MB
10 - Deep Learning PyTorch Introduction/007 Deep Learning with Pytorch Optimizers.mp4 10.2 MB
06 - Natural Language Processing + Regularization/015 L2 Loss Ridge Regression intro.mp4 10.0 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/005 PyTorch Lightning Model.mp4 9.4 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/002 Kaggle problem description.mp4 9.2 MB
01 - Introduction/004 Jupyter Notebooks.mp4 8.7 MB
14 - Bayesian Learning and probabilistic programming/011 Variational Bayes Intro.mp4 8.6 MB
02 - Basic python + Pandas + Plotting/019 Line plot.mp4 8.6 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/013 Cross Entropy Loss for Imbalanced Classes.mp4 8.5 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/012 Setting up PyTorch Lightning for training.mp4 8.4 MB
06 - Natural Language Processing + Regularization/013 Introduction.mp4 8.4 MB
13 - Deep Learning Transformers and BERT/004 BERT - The theory.mp4 8.1 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/011 Deep Learning Transfer Learning Model with ResNet.mp4 8.0 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/010 Train vs Test Augmentations + DataLoader parameters.mp4 7.7 MB
15 - Model Deployment/002 Saving Models.mp4 7.6 MB
14 - Bayesian Learning and probabilistic programming/013 Variational Bayesian Inference Result Analysis.mp4 7.4 MB
14 - Bayesian Learning and probabilistic programming/015 Deep Bayesian Networks.mp4 7.3 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/014 PyTorch Test dataset setup and evaluation.mp4 7.1 MB
13 - Deep Learning Transformers and BERT/005 Kaggle Multi-lingual Toxic Comment Classification Challenge.mp4 6.8 MB
04 - Machine Learning Classification + Time Series + Model Diagnostics/001 Kaggle part 1.mp4 6.7 MB
02 - Basic python + Pandas + Plotting/008 Dictionaries again.mp4 6.2 MB
06 - Natural Language Processing + Regularization/003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory.mp4 6.1 MB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/001 Intro.mp4 6.0 MB
15 - Model Deployment/005 Streamlit Intro.mp4 6.0 MB
09 - Deep Learning Recurrent Neural Nets/006 Deep Learning - Stacking LSTMs + GRUs.mp4 5.0 MB
02 - Basic python + Pandas + Plotting/010 Intro.mp4 5.0 MB
10 - Deep Learning PyTorch Introduction/009 Pytorch in GPUs.mp4 5.0 MB
03 - Machine Learning Numpy + Scikit Learn/013 Random Forest theory.mp4 4.8 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/001 Transfer Learning Introduction.mp4 4.5 MB
11 - Deep Learning Transfer Learning with PyTorch Lightning/007 Deep Learning for Cassava Leaf Classification.mp4 4.1 MB
13 - Deep Learning Transformers and BERT/001 Introduction to Transformers.mp4 3.4 MB
02 - Basic python + Pandas + Plotting/001 Intro.mp4 2.9 MB
02 - Basic python + Pandas + Plotting/31237618-03-0-plotting.zip 2.8 MB
07 - Deep Learning/32725408-09-tensorflow.zip 2.7 MB
06 - Natural Language Processing + Regularization/31762302-06-0-reguralisation.zip 2.6 MB
15 - Model Deployment/001 Intro.mp4 2.5 MB
10 - Deep Learning PyTorch Introduction/001 Introduction.mp4 2.2 MB
03 - Machine Learning Numpy + Scikit Learn/001 Your reviews are important to me!.mp4 2.0 MB
14 - Bayesian Learning and probabilistic programming/31919076-bayesian-inference.zip 1.8 MB
07 - Deep Learning/001 Intro.mp4 632.6 KB
02 - Basic python + Pandas + Plotting/34142844-04-pairplots.ipynb 200.5 KB
03 - Machine Learning Numpy + Scikit Learn/012 CART part 2_en.vtt 20.5 KB
03 - Machine Learning Numpy + Scikit Learn/005 Kmeans part 2_en.vtt 19.7 KB
13 - Deep Learning Transformers and BERT/008 Pytorch Lightning + DistilBERT for classification_en.vtt 17.3 KB
03 - Machine Learning Numpy + Scikit Learn/003 Gradient Descent_en.vtt 16.6 KB
07 - Deep Learning/004 Tensorflow + Keras demo problem 1_en.vtt 16.4 KB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/009 Semantic Segmentation training with PyTorch Lightning_en.vtt 16.2 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/005 Titanic dataset_en.vtt 15.2 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/007 Sklearn classification_en.vtt 14.5 KB
09 - Deep Learning Recurrent Neural Nets/003 Word2Vec keras Model API_en.vtt 13.3 KB
09 - Deep Learning Recurrent Neural Nets/010 Sequence to Sequence models Prediction step_en.vtt 13.1 KB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/008 Nose Tip detection with CNNs_en.vtt 12.5 KB
03 - Machine Learning Numpy + Scikit Learn/009 Linear Regresson Part 1_en.vtt 12.2 KB
03 - Machine Learning Numpy + Scikit Learn/004 Kmeans part 1_en.vtt 11.8 KB
09 - Deep Learning Recurrent Neural Nets/005 Deep Learning - Long Short Term Memory (LSTM) Nets_en.vtt 11.8 KB
09 - Deep Learning Recurrent Neural Nets/007 Transfer Learning - GLOVE vectors_en.vtt 11.4 KB
02 - Basic python + Pandas + Plotting/011 Pandas simple functions_en.vtt 11.4 KB
03 - Machine Learning Numpy + Scikit Learn/010 Linear Regression Part 2_en.vtt 11.2 KB
13 - Deep Learning Transformers and BERT/006 Tokenizers and data prep for BERT models_en.vtt 10.8 KB
13 - Deep Learning Transformers and BERT/007 Distilbert (Smaller BERT) model_en.vtt 10.8 KB
02 - Basic python + Pandas + Plotting/005 Numpy functions_en.vtt 10.6 KB
09 - Deep Learning Recurrent Neural Nets/004 Recurrent Neural Nets - Theory_en.vtt 10.6 KB
09 - Deep Learning Recurrent Neural Nets/002 Kaggle + Word2Vec_en.vtt 10.5 KB
05 - Unsupervised Learning/002 Fashion MNIST PCA_en.vtt 10.5 KB
14 - Bayesian Learning and probabilistic programming/002 Bayesian Learning Distributions_en.vtt 10.5 KB
07 - Deep Learning/007 MNIST and Softmax_en.vtt 10.4 KB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/007 Cifar-10_en.vtt 10.1 KB
06 - Natural Language Processing + Regularization/004 Financial News Sentiment Classifier_en.vtt 10.0 KB
14 - Bayesian Learning and probabilistic programming/007 Bayesian Linear Regression with pymc3_en.vtt 10.0 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/018 Stratified K Fold_en.vtt 9.9 KB
06 - Natural Language Processing + Regularization/009 Feature Extraction with Spacy (using Pandas)_en.vtt 9.8 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/012 FB Prophet part 1_en.vtt 9.8 KB
03 - Machine Learning Numpy + Scikit Learn/015 Gradient Boosted Machines_en.vtt 9.7 KB
03 - Machine Learning Numpy + Scikit Learn/006 Broadcasting_en.vtt 9.6 KB
10 - Deep Learning PyTorch Introduction/010 Deep Learning Intro to Pytorch Lightning_en.vtt 9.3 KB
14 - Bayesian Learning and probabilistic programming/009 Bayesian Rolling regression - pymc3 way_en.vtt 9.3 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/019 Area Under Curve (AUC) Part 1_en.vtt 9.2 KB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/007 PyTorch Weighted CrossEntropy Loss_en.vtt 9.1 KB
05 - Unsupervised Learning/001 Principal Component Analysis (PCA) theory_en.vtt 9.0 KB
14 - Bayesian Learning and probabilistic programming/003 Bayes rule for population mean estimation_en.vtt 9.0 KB
13 - Deep Learning Transformers and BERT/002 The illustrated Transformer (blogpost by Jay Alammar)_en.vtt 8.9 KB
14 - Bayesian Learning and probabilistic programming/004 Bayesian learning Population estimation pymc3 way_en.vtt 8.9 KB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/006 PyTorch Hooks Step through with breakpoints_en.vtt 8.8 KB
09 - Deep Learning Recurrent Neural Nets/009 Sequence to Sequence model + Keras Model API_en.vtt 8.7 KB
10 - Deep Learning PyTorch Introduction/005 Deep Learning with Pytorch Loss functions_en.vtt 8.7 KB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/003 Keras Conv2D layer_en.vtt 8.6 KB
14 - Bayesian Learning and probabilistic programming/001 Introduction and Terminology_en.vtt 8.3 KB
09 - Deep Learning Recurrent Neural Nets/001 Word2vec and Embeddings_en.vtt 8.3 KB
07 - Deep Learning/011 Batch Norm Theory_en.vtt 8.3 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/017 Cross Validation_en.vtt 8.3 KB
02 - Basic python + Pandas + Plotting/015 Pandas map and apply_en.vtt 8.2 KB
10 - Deep Learning PyTorch Introduction/006 Deep Learning with Pytorch Stochastic Gradient Descent_en.vtt 8.1 KB
14 - Bayesian Learning and probabilistic programming/005 Coin Toss Example with Pymc3_en.vtt 8.0 KB
09 - Deep Learning Recurrent Neural Nets/008 Sequence to Sequence Introduction + Data Prep_en.vtt 8.0 KB
02 - Basic python + Pandas + Plotting/024 Seaborn + pair plots_en.vtt 7.9 KB
06 - Natural Language Processing + Regularization/016 Ridge regression (L2 penalised regression)_en.vtt 7.9 KB
05 - Unsupervised Learning/006 Gaussian Mixture Models (GMM) theory_en.vtt 7.9 KB
02 - Basic python + Pandas + Plotting/021 Histograms_en.vtt 7.9 KB
06 - Natural Language Processing + Regularization/005 NLTK + Stemming_en.vtt 7.8 KB
02 - Basic python + Pandas + Plotting/013 Pandas loc and iloc_en.vtt 7.6 KB
05 - Unsupervised Learning/003 K-means_en.vtt 7.6 KB
15 - Model Deployment/004 FastAPI serving model_en.vtt 7.5 KB
14 - Bayesian Learning and probabilistic programming/012 Variational Bayes Linear Classification_en.vtt 7.5 KB
15 - Model Deployment/007 CLIP model_en.vtt 7.3 KB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/005 PyTorch Hooks_en.vtt 7.3 KB
05 - Unsupervised Learning/004 Other clustering methods_en.vtt 7.2 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/011 Loss functions_en.vtt 7.2 KB
06 - Natural Language Processing + Regularization/017 S&P500 data preparation for L1 loss_en.vtt 7.1 KB
02 - Basic python + Pandas + Plotting/016 Pandas groupby_en.vtt 7.0 KB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/005 Dropout theory and code_en.vtt 7.0 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/016 Overfitting_en.vtt 7.0 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/020 Area Under Curve (AUC) Part 2_en.vtt 7.0 KB
05 - Unsupervised Learning/005 DBSCAN theory_en.vtt 6.9 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/003 Theory part 1_en.vtt 6.7 KB
03 - Machine Learning Numpy + Scikit Learn/014 Random Forest Code_en.vtt 6.7 KB
03 - Machine Learning Numpy + Scikit Learn/011 Classification and Regression Trees_en.vtt 6.4 KB
02 - Basic python + Pandas + Plotting/002 Basic Data Structures_en.vtt 6.4 KB
02 - Basic python + Pandas + Plotting/022 Scatter Plots_en.vtt 6.4 KB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/003 Unet Architecture overview_en.vtt 6.4 KB
11 - Deep Learning Transfer Learning with PyTorch Lightning/006 PyTorch Lightning Trainer + Model evaluation_en.vtt 6.3 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/004 Theory part 2 + code_en.vtt 6.3 KB
02 - Basic python + Pandas + Plotting/012 Pandas Subsetting_en.vtt 6.3 KB
01 - Introduction/002 How to tackle this course_en.vtt 6.2 KB
07 - Deep Learning/002 DL theory part 1_en.vtt 6.1 KB
06 - Natural Language Processing + Regularization/014 MSE recap_en.vtt 6.1 KB
15 - Model Deployment/006 Streamlit functions_en.vtt 6.1 KB
02 - Basic python + Pandas + Plotting/023 Subplots_en.vtt 6.0 KB
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/002 Coco Dataset + Augmentations for Segmentation with Torchvision_en.vtt 5.9 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/010 Intro_en.vtt 5.9 KB
11 - Deep Learning Transfer Learning with PyTorch Lightning/009 Data Augmentation with Torchvision Transforms_en.vtt 5.9 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/014 Theory behind FB Prophet_en.vtt 5.9 KB
06 - Natural Language Processing + Regularization/011 Over-sampling_en.vtt 5.8 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/008 Dealing with missing values_en.vtt 5.8 KB
10 - Deep Learning PyTorch Introduction/003 Pytorch Dataset and DataLoaders_en.vtt 5.7 KB
10 - Deep Learning PyTorch Introduction/004 Deep Learning with PyTorch nn.Sequential models_en.vtt 5.7 KB
07 - Deep Learning/010 Batch Norm_en.vtt 5.7 KB
06 - Natural Language Processing + Regularization/018 L1 Penalised Regression (Lasso)_en.vtt 5.6 KB
14 - Bayesian Learning and probabilistic programming/008 Bayesian Rolling Regression - Problem setup_en.vtt 5.6 KB
13 - Deep Learning Transformers and BERT/003 Encoder Transformer Models The Maths_en.vtt 5.6 KB
06 - Natural Language Processing + Regularization/008 Spacy intro_en.vtt 5.6 KB
02 - Basic python + Pandas + Plotting/004 Python functions (methods)_en.vtt 5.6 KB
07 - Deep Learning/009 Softmax theory_en.vtt 5.5 KB
07 - Deep Learning/005 Activation functions_en.vtt 5.5 KB
10 - Deep Learning PyTorch Introduction/008 Pytorch Model API_en.vtt 5.5 KB
07 - Deep Learning/006 First example with Relu_en.vtt 5.4 KB
11 - Deep Learning Transfer Learning with PyTorch Lightning/015 WandB for logging experiments_en.vtt 5.4 KB
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/006 MaxPool (and comparison to stride)_en.vtt 5.4 KB
06 - Natural Language Processing + Regularization/001 Intro_en.vtt 5.4 KB
14 - Bayesian Learning and probabilistic programming/010 Bayesian Rolling Regression - forecasting_en.vtt 5.3 KB
15 - Model Deployment/003 FastAPI intro_en.vtt 5.3 KB
04 - Machine Learning Classification + Time Series + Model Diagnostics/006 Sklearn classification prelude_en.vtt 5.3 KB
02 - Basic python + Pandas + Plotting/014 Pandas loc and iloc 2_en.vtt 5.2 KB
10 - Deep Learning PyTorch Introduction/002 Pytorch TensorDataset_en.vtt 5.0 KB
03 - Machine Learning Numpy + Scikit Learn/008 Intro_en.vtt 4.9 KB
01 - Introduction/004 Jupyter Notebooks_en.vtt 4.9 KB
06 - Natural Language Processing + Regularization/002 Stop words and Term Frequency_en.vtt 4.9 KB
11 - Deep Learning Transfer Learning with PyTorch Lightning/008 Cassava Leaf Dataset_en.vtt 4.8 KB
01 - Introduction/003 Installations and sign ups_en.vtt 4.8 KB
14 - Bayesian Learning and probabilistic programming/006 Data Setup for Bayesian Linear Regression_en.vtt 4.7 KB
11 - Deep Learning Transfer Learning with PyTorch Lightning/004 PyTorch transfer learning with ResNet_en.vtt 4.4 KB
06 - Natural Language Processing + Regularization/010 Classification Example_en.vtt 4.3 KB
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