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03 - Machine Learning Numpy + Scikit Learn/012 CART part 2.mp4 174.6 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/009 Semantic Segmentation training with PyTorch Lightning.mp4 136.5 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/005 Titanic dataset.mp4 122.0 MB09 - Deep Learning Recurrent Neural Nets/010 Sequence to Sequence models Prediction step.mp4 109.8 MB13 - Deep Learning Transformers and BERT/008 Pytorch Lightning + DistilBERT for classification.mp4 107.7 MB05 - Unsupervised Learning/002 Fashion MNIST PCA.mp4 107.1 MB09 - Deep Learning Recurrent Neural Nets/005 Deep Learning - Long Short Term Memory (LSTM) Nets.mp4 95.4 MB03 - Machine Learning Numpy + Scikit Learn/009 Linear Regresson Part 1.mp4 94.9 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/007 Sklearn classification.mp4 94.4 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/019 Area Under Curve (AUC) Part 1.mp4 88.2 MB09 - Deep Learning Recurrent Neural Nets/008 Sequence to Sequence Introduction + Data Prep.mp4 84.0 MB10 - Deep Learning PyTorch Introduction/006 Deep Learning with Pytorch Stochastic Gradient Descent.mp4 83.3 MB03 - Machine Learning Numpy + Scikit Learn/004 Kmeans part 1.mp4 82.2 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/012 FB Prophet part 1.mp4 81.8 MB06 - Natural Language Processing + Regularization/009 Feature Extraction with Spacy (using Pandas).mp4 80.2 MB09 - Deep Learning Recurrent Neural Nets/007 Transfer Learning - GLOVE vectors.mp4 78.2 MB03 - Machine Learning Numpy + Scikit Learn/010 Linear Regression Part 2.mp4 75.0 MB14 - Bayesian Learning and probabilistic programming/005 Coin Toss Example with Pymc3.mp4 74.1 MB14 - Bayesian Learning and probabilistic programming/004 Bayesian learning Population estimation pymc3 way.mp4 74.0 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/008 Nose Tip detection with CNNs.mp4 72.0 MB03 - Machine Learning Numpy + Scikit Learn/015 Gradient Boosted Machines.mp4 70.9 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/006 PyTorch Hooks Step through with breakpoints.mp4 70.8 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/007 PyTorch Weighted CrossEntropy Loss.mp4 68.4 MB03 - Machine Learning Numpy + Scikit Learn/005 Kmeans part 2.mp4 66.3 MB02 - Basic python + Pandas + Plotting/005 Numpy functions.mp4 65.5 MB14 - Bayesian Learning and probabilistic programming/007 Bayesian Linear Regression with pymc3.mp4 63.0 MB07 - Deep Learning/009 Softmax theory.mp4 61.2 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/018 Stratified K Fold.mp4 60.9 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/009 Data Augmentation with Torchvision Transforms.mp4 59.3 MB07 - Deep Learning/007 MNIST and Softmax.mp4 58.5 MB14 - Bayesian Learning and probabilistic programming/009 Bayesian Rolling regression - pymc3 way.mp4 57.4 MB07 - Deep Learning/011 Batch Norm Theory.mp4 56.5 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/017 Cross Validation.mp4 56.3 MB10 - Deep Learning PyTorch Introduction/005 Deep Learning with Pytorch Loss functions.mp4 55.0 MB10 - Deep Learning PyTorch Introduction/010 Deep Learning Intro to Pytorch Lightning.mp4 54.9 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/008 Dealing with missing values.mp4 53.2 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/006 PyTorch Lightning Trainer + Model evaluation.mp4 52.7 MB14 - Bayesian Learning and probabilistic programming/003 Bayes rule for population mean estimation.mp4 52.6 MB02 - Basic python + Pandas + Plotting/024 Seaborn + pair plots.mp4 52.1 MB01 - Introduction/002 How to tackle this course.mp4 51.2 MB13 - Deep Learning Transformers and BERT/007 Distilbert (Smaller BERT) model.mp4 51.2 MB05 - Unsupervised Learning/004 Other clustering methods.mp4 50.4 MB06 - Natural Language Processing + Regularization/016 Ridge regression (L2 penalised regression).mp4 49.3 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/011 Loss functions.mp4 48.7 MB06 - Natural Language Processing + Regularization/005 NLTK + Stemming.mp4 47.8 MB02 - Basic python + Pandas + Plotting/020 Plot multiple lines.mp4 47.6 MB09 - Deep Learning Recurrent Neural Nets/003 Word2Vec keras Model API.mp4 47.4 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/003 Keras Conv2D layer.mp4 46.6 MB14 - Bayesian Learning and probabilistic programming/012 Variational Bayes Linear Classification.mp4 46.4 MB09 - Deep Learning Recurrent Neural Nets/001 Word2vec and Embeddings.mp4 46.1 MB03 - Machine Learning Numpy + Scikit Learn/003 Gradient Descent.mp4 45.5 MB07 - Deep Learning/004 Tensorflow + Keras demo problem 1.mp4 45.4 MB01 - Introduction/003 Installations and sign ups.mp4 44.9 MB02 - Basic python + Pandas + Plotting/013 Pandas loc and iloc.mp4 43.8 MB01 - Introduction/001 Introduction.mp4 43.8 MB02 - Basic python + Pandas + Plotting/011 Pandas simple functions.mp4 40.2 MB03 - Machine Learning Numpy + Scikit Learn/014 Random Forest Code.mp4 38.5 MB14 - Bayesian Learning and probabilistic programming/002 Bayesian Learning Distributions.mp4 37.7 MB03 - Machine Learning Numpy + Scikit Learn/008 Intro.mp4 37.1 MB10 - Deep Learning PyTorch Introduction/003 Pytorch Dataset and DataLoaders.mp4 37.1 MB06 - Natural Language Processing + Regularization/004 Financial News Sentiment Classifier.mp4 35.3 MB10 - Deep Learning PyTorch Introduction/008 Pytorch Model API.mp4 34.9 MB06 - Natural Language Processing + Regularization/008 Spacy intro.mp4 34.8 MB06 - Natural Language Processing + Regularization/011 Over-sampling.mp4 34.4 MB07 - Deep Learning/006 First example with Relu.mp4 34.2 MB02 - Basic python + Pandas + Plotting/015 Pandas map and apply.mp4 33.0 MB06 - Natural Language Processing + Regularization/018 L1 Penalised Regression (Lasso).mp4 32.9 MB09 - Deep Learning Recurrent Neural Nets/009 Sequence to Sequence model + Keras Model API.mp4 32.0 MB14 - Bayesian Learning and probabilistic programming/010 Bayesian Rolling Regression - forecasting.mp4 31.8 MB15 - Model Deployment/004 FastAPI serving model.mp4 30.7 MB13 - Deep Learning Transformers and BERT/006 Tokenizers and data prep for BERT models.mp4 30.5 MB13 - Deep Learning Transformers and BERT/003 Encoder Transformer Models The Maths.mp4 30.1 MB09 - Deep Learning Recurrent Neural Nets/002 Kaggle + Word2Vec.mp4 29.1 MB02 - Basic python + Pandas + Plotting/004 Python functions (methods).mp4 28.9 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/007 Cifar-10.mp4 28.6 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/004 Theory part 2 + code.mp4 28.6 MB03 - Machine Learning Numpy + Scikit Learn/006 Broadcasting.mp4 28.5 MB
01 - Introduction/30889860-course-code-material.zip 27.5 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/001 Introduction.mp4 26.5 MB06 - Natural Language Processing + Regularization/017 S&P500 data preparation for L1 loss.mp4 26.4 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/005 PyTorch Hooks.mp4 25.9 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/013 FB Prophet part 2.mp4 25.6 MB06 - Natural Language Processing + Regularization/010 Classification Example.mp4 25.3 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/005 Dropout theory and code.mp4 24.8 MB13 - Deep Learning Transformers and BERT/002 The illustrated Transformer (blogpost by Jay Alammar).mp4 24.7 MB06 - Natural Language Processing + Regularization/019 L1 L2 Penalty theory why it works.mp4 24.3 MB07 - Deep Learning/003 DL theory part 2.mp4 23.9 MB05 - Unsupervised Learning/003 K-means.mp4 23.4 MB02 - Basic python + Pandas + Plotting/012 Pandas Subsetting.mp4 23.1 MB02 - Basic python + Pandas + Plotting/002 Basic Data Structures.mp4 23.0 MB02 - Basic python + Pandas + Plotting/021 Histograms.mp4 22.7 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/015 WandB for logging experiments.mp4 22.6 MB15 - Model Deployment/006 Streamlit functions.mp4 21.8 MB05 - Unsupervised Learning/001 Principal Component Analysis (PCA) theory.mp4 21.5 MB05 - Unsupervised Learning/006 Gaussian Mixture Models (GMM) theory.mp4 21.0 MB03 - Machine Learning Numpy + Scikit Learn/011 Classification and Regression Trees.mp4 21.0 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/002 Fashion MNIST feed forward net for benchmarking.mp4 20.6 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/020 Area Under Curve (AUC) Part 2.mp4 20.4 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/016 Overfitting.mp4 20.3 MB09 - Deep Learning Recurrent Neural Nets/004 Recurrent Neural Nets - Theory.mp4 20.0 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/002 Coco Dataset + Augmentations for Segmentation with Torchvision.mp4 19.8 MB02 - Basic python + Pandas + Plotting/003 Dictionaries.mp4 19.7 MB15 - Model Deployment/007 CLIP model.mp4 19.7 MB02 - Basic python + Pandas + Plotting/022 Scatter Plots.mp4 19.5 MB02 - Basic python + Pandas + Plotting/016 Pandas groupby.mp4 19.2 MB06 - Natural Language Processing + Regularization/014 MSE recap.mp4 19.2 MB14 - Bayesian Learning and probabilistic programming/001 Introduction and Terminology.mp4 19.0 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/006 MaxPool (and comparison to stride).mp4 18.5 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/004 Model fitting and discussion of results.mp4 18.3 MB07 - Deep Learning/002 DL theory part 1.mp4 18.1 MB14 - Bayesian Learning and probabilistic programming/006 Data Setup for Bayesian Linear Regression.mp4 17.9 MB07 - Deep Learning/010 Batch Norm.mp4 17.9 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/014 Theory behind FB Prophet.mp4 17.7 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/008 Weights and Biases Logging images.mp4 16.6 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/004 PyTorch transfer learning with ResNet.mp4 16.2 MB07 - Deep Learning/005 Activation functions.mp4 16.1 MB02 - Basic python + Pandas + Plotting/023 Subplots.mp4 16.1 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/008 Cassava Leaf Dataset.mp4 16.0 MB14 - Bayesian Learning and probabilistic programming/008 Bayesian Rolling Regression - Problem setup.mp4 15.6 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/003 PyTorch datasets + Torchvision.mp4 15.4 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/003 Unet Architecture overview.mp4 15.4 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/006 Sklearn classification prelude.mp4 15.0 MB02 - Basic python + Pandas + Plotting/014 Pandas loc and iloc 2.mp4 14.5 MB06 - Natural Language Processing + Regularization/006 N-grams.mp4 14.5 MB16 - Final Thoughts/001 Some advice on your journey.mp4 14.2 MB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/004 PyTorch Model Architecture.mp4 14.2 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/003 Theory part 1.mp4 14.2 MB05 - Unsupervised Learning/005 DBSCAN theory.mp4 13.9 MB02 - Basic python + Pandas + Plotting/006 Conditional statements.mp4 13.2 MB06 - Natural Language Processing + Regularization/007 Word (feature) importance.mp4 13.0 MB10 - Deep Learning PyTorch Introduction/002 Pytorch TensorDataset.mp4 13.0 MB02 - Basic python + Pandas + Plotting/007 For loops.mp4 13.0 MB15 - Model Deployment/003 FastAPI intro.mp4 12.2 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/010 Intro.mp4 12.0 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/002 Kaggle part 2.mp4 11.7 MB14 - Bayesian Learning and probabilistic programming/014 Minibatch Variational Bayes.mp4 11.6 MB10 - Deep Learning PyTorch Introduction/004 Deep Learning with PyTorch nn.Sequential models.mp4 11.6 MB06 - Natural Language Processing + Regularization/002 Stop words and Term Frequency.mp4 11.2 MB14 - Bayesian Learning and probabilistic programming/016 Deep Bayesian Networks - analysis.mp4 11.0 MB06 - Natural Language Processing + Regularization/001 Intro.mp4 10.9 MB07 - Deep Learning/008 Deep Learning Input Normalisation.mp4 10.9 MB10 - Deep Learning PyTorch Introduction/007 Deep Learning with Pytorch Optimizers.mp4 10.7 MB06 - Natural Language Processing + Regularization/015 L2 Loss Ridge Regression intro.mp4 10.5 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/005 PyTorch Lightning Model.mp4 9.9 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/002 Kaggle problem description.mp4 9.6 MB01 - Introduction/004 Jupyter Notebooks.mp4 9.1 MB14 - Bayesian Learning and probabilistic programming/011 Variational Bayes Intro.mp4 9.1 MB02 - Basic python + Pandas + Plotting/019 Line plot.mp4 9.0 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/013 Cross Entropy Loss for Imbalanced Classes.mp4 8.9 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/012 Setting up PyTorch Lightning for training.mp4 8.8 MB06 - Natural Language Processing + Regularization/013 Introduction.mp4 8.8 MB13 - Deep Learning Transformers and BERT/004 BERT - The theory.mp4 8.5 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/011 Deep Learning Transfer Learning Model with ResNet.mp4 8.4 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/010 Train vs Test Augmentations + DataLoader parameters.mp4 8.1 MB15 - Model Deployment/002 Saving Models.mp4 7.9 MB14 - Bayesian Learning and probabilistic programming/013 Variational Bayesian Inference Result Analysis.mp4 7.7 MB14 - Bayesian Learning and probabilistic programming/015 Deep Bayesian Networks.mp4 7.6 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/014 PyTorch Test dataset setup and evaluation.mp4 7.4 MB13 - Deep Learning Transformers and BERT/005 Kaggle Multi-lingual Toxic Comment Classification Challenge.mp4 7.1 MB04 - Machine Learning Classification + Time Series + Model Diagnostics/001 Kaggle part 1.mp4 7.1 MB02 - Basic python + Pandas + Plotting/008 Dictionaries again.mp4 6.5 MB06 - Natural Language Processing + Regularization/003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory.mp4 6.3 MB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/001 Intro.mp4 6.3 MB15 - Model Deployment/005 Streamlit Intro.mp4 6.2 MB09 - Deep Learning Recurrent Neural Nets/006 Deep Learning - Stacking LSTMs + GRUs.mp4 5.3 MB02 - Basic python + Pandas + Plotting/010 Intro.mp4 5.3 MB10 - Deep Learning PyTorch Introduction/009 Pytorch in GPUs.mp4 5.2 MB03 - Machine Learning Numpy + Scikit Learn/013 Random Forest theory.mp4 5.1 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/001 Transfer Learning Introduction.mp4 4.7 MB11 - Deep Learning Transfer Learning with PyTorch Lightning/007 Deep Learning for Cassava Leaf Classification.mp4 4.3 MB13 - Deep Learning Transformers and BERT/001 Introduction to Transformers.mp4 3.6 MB02 - Basic python + Pandas + Plotting/001 Intro.mp4 3.0 MB02 - Basic python + Pandas + Plotting/31237618-03-0-plotting.zip 2.9 MB07 - Deep Learning/32725408-09-tensorflow.zip 2.8 MB06 - Natural Language Processing + Regularization/31762302-06-0-reguralisation.zip 2.7 MB15 - Model Deployment/001 Intro.mp4 2.6 MB10 - Deep Learning PyTorch Introduction/001 Introduction.mp4 2.3 MB03 - Machine Learning Numpy + Scikit Learn/001 Your reviews are important to me!.mp4 2.1 MB14 - Bayesian Learning and probabilistic programming/31919076-bayesian-inference.zip 1.9 MB07 - Deep Learning/001 Intro.mp4 647.8 kB
02 - Basic python + Pandas + Plotting/34142844-04-pairplots.ipynb 205.3 kB03 - Machine Learning Numpy + Scikit Learn/012 CART part 2_en.vtt 21.0 kB03 - Machine Learning Numpy + Scikit Learn/005 Kmeans part 2_en.vtt 20.2 kB13 - Deep Learning Transformers and BERT/008 Pytorch Lightning + DistilBERT for classification_en.vtt 17.7 kB03 - Machine Learning Numpy + Scikit Learn/003 Gradient Descent_en.vtt 17.0 kB07 - Deep Learning/004 Tensorflow + Keras demo problem 1_en.vtt 16.8 kB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/009 Semantic Segmentation training with PyTorch Lightning_en.vtt 16.6 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/005 Titanic dataset_en.vtt 15.6 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/007 Sklearn classification_en.vtt 14.8 kB09 - Deep Learning Recurrent Neural Nets/003 Word2Vec keras Model API_en.vtt 13.6 kB09 - Deep Learning Recurrent Neural Nets/010 Sequence to Sequence models Prediction step_en.vtt 13.4 kB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/008 Nose Tip detection with CNNs_en.vtt 12.8 kB03 - Machine Learning Numpy + Scikit Learn/009 Linear Regresson Part 1_en.vtt 12.5 kB03 - Machine Learning Numpy + Scikit Learn/004 Kmeans part 1_en.vtt 12.1 kB09 - Deep Learning Recurrent Neural Nets/005 Deep Learning - Long Short Term Memory (LSTM) Nets_en.vtt 12.1 kB09 - Deep Learning Recurrent Neural Nets/007 Transfer Learning - GLOVE vectors_en.vtt 11.7 kB02 - Basic python + Pandas + Plotting/011 Pandas simple functions_en.vtt 11.7 kB03 - Machine Learning Numpy + Scikit Learn/010 Linear Regression Part 2_en.vtt 11.5 kB13 - Deep Learning Transformers and BERT/006 Tokenizers and data prep for BERT models_en.vtt 11.0 kB13 - Deep Learning Transformers and BERT/007 Distilbert (Smaller BERT) model_en.vtt 11.0 kB02 - Basic python + Pandas + Plotting/005 Numpy functions_en.vtt 10.9 kB09 - Deep Learning Recurrent Neural Nets/004 Recurrent Neural Nets - Theory_en.vtt 10.8 kB09 - Deep Learning Recurrent Neural Nets/002 Kaggle + Word2Vec_en.vtt 10.8 kB05 - Unsupervised Learning/002 Fashion MNIST PCA_en.vtt 10.7 kB14 - Bayesian Learning and probabilistic programming/002 Bayesian Learning Distributions_en.vtt 10.7 kB07 - Deep Learning/007 MNIST and Softmax_en.vtt 10.7 kB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/007 Cifar-10_en.vtt 10.3 kB06 - Natural Language Processing + Regularization/004 Financial News Sentiment Classifier_en.vtt 10.2 kB14 - Bayesian Learning and probabilistic programming/007 Bayesian Linear Regression with pymc3_en.vtt 10.2 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/018 Stratified K Fold_en.vtt 10.2 kB06 - Natural Language Processing + Regularization/009 Feature Extraction with Spacy (using Pandas)_en.vtt 10.1 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/012 FB Prophet part 1_en.vtt 10.0 kB03 - Machine Learning Numpy + Scikit Learn/015 Gradient Boosted Machines_en.vtt 9.9 kB03 - Machine Learning Numpy + Scikit Learn/006 Broadcasting_en.vtt 9.9 kB10 - Deep Learning PyTorch Introduction/010 Deep Learning Intro to Pytorch Lightning_en.vtt 9.5 kB14 - Bayesian Learning and probabilistic programming/009 Bayesian Rolling regression - pymc3 way_en.vtt 9.5 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/019 Area Under Curve (AUC) Part 1_en.vtt 9.4 kB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/007 PyTorch Weighted CrossEntropy Loss_en.vtt 9.3 kB05 - Unsupervised Learning/001 Principal Component Analysis (PCA) theory_en.vtt 9.2 kB14 - Bayesian Learning and probabilistic programming/003 Bayes rule for population mean estimation_en.vtt 9.2 kB13 - Deep Learning Transformers and BERT/002 The illustrated Transformer (blogpost by Jay Alammar)_en.vtt 9.2 kB14 - Bayesian Learning and probabilistic programming/004 Bayesian learning Population estimation pymc3 way_en.vtt 9.1 kB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/006 PyTorch Hooks Step through with breakpoints_en.vtt 9.0 kB09 - Deep Learning Recurrent Neural Nets/009 Sequence to Sequence model + Keras Model API_en.vtt 8.9 kB10 - Deep Learning PyTorch Introduction/005 Deep Learning with Pytorch Loss functions_en.vtt 8.9 kB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/003 Keras Conv2D layer_en.vtt 8.8 kB14 - Bayesian Learning and probabilistic programming/001 Introduction and Terminology_en.vtt 8.5 kB09 - Deep Learning Recurrent Neural Nets/001 Word2vec and Embeddings_en.vtt 8.5 kB07 - Deep Learning/011 Batch Norm Theory_en.vtt 8.5 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/017 Cross Validation_en.vtt 8.5 kB02 - Basic python + Pandas + Plotting/015 Pandas map and apply_en.vtt 8.4 kB10 - Deep Learning PyTorch Introduction/006 Deep Learning with Pytorch Stochastic Gradient Descent_en.vtt 8.3 kB14 - Bayesian Learning and probabilistic programming/005 Coin Toss Example with Pymc3_en.vtt 8.2 kB09 - Deep Learning Recurrent Neural Nets/008 Sequence to Sequence Introduction + Data Prep_en.vtt 8.2 kB02 - Basic python + Pandas + Plotting/024 Seaborn + pair plots_en.vtt 8.1 kB06 - Natural Language Processing + Regularization/016 Ridge regression (L2 penalised regression)_en.vtt 8.1 kB05 - Unsupervised Learning/006 Gaussian Mixture Models (GMM) theory_en.vtt 8.1 kB02 - Basic python + Pandas + Plotting/021 Histograms_en.vtt 8.1 kB06 - Natural Language Processing + Regularization/005 NLTK + Stemming_en.vtt 8.0 kB02 - Basic python + Pandas + Plotting/013 Pandas loc and iloc_en.vtt 7.8 kB05 - Unsupervised Learning/003 K-means_en.vtt 7.8 kB15 - Model Deployment/004 FastAPI serving model_en.vtt 7.7 kB14 - Bayesian Learning and probabilistic programming/012 Variational Bayes Linear Classification_en.vtt 7.7 kB15 - Model Deployment/007 CLIP model_en.vtt 7.5 kB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/005 PyTorch Hooks_en.vtt 7.5 kB05 - Unsupervised Learning/004 Other clustering methods_en.vtt 7.3 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/011 Loss functions_en.vtt 7.3 kB06 - Natural Language Processing + Regularization/017 S&P500 data preparation for L1 loss_en.vtt 7.3 kB02 - Basic python + Pandas + Plotting/016 Pandas groupby_en.vtt 7.2 kB08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/005 Dropout theory and code_en.vtt 7.2 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/016 Overfitting_en.vtt 7.2 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/020 Area Under Curve (AUC) Part 2_en.vtt 7.1 kB05 - Unsupervised Learning/005 DBSCAN theory_en.vtt 7.1 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/003 Theory part 1_en.vtt 6.9 kB03 - Machine Learning Numpy + Scikit Learn/014 Random Forest Code_en.vtt 6.8 kB03 - Machine Learning Numpy + Scikit Learn/011 Classification and Regression Trees_en.vtt 6.6 kB02 - Basic python + Pandas + Plotting/002 Basic Data Structures_en.vtt 6.6 kB02 - Basic python + Pandas + Plotting/022 Scatter Plots_en.vtt 6.5 kB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/003 Unet Architecture overview_en.vtt 6.5 kB11 - Deep Learning Transfer Learning with PyTorch Lightning/006 PyTorch Lightning Trainer + Model evaluation_en.vtt 6.5 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/004 Theory part 2 + code_en.vtt 6.4 kB02 - Basic python + Pandas + Plotting/012 Pandas Subsetting_en.vtt 6.4 kB01 - Introduction/002 How to tackle this course_en.vtt 6.4 kB07 - Deep Learning/002 DL theory part 1_en.vtt 6.3 kB06 - Natural Language Processing + Regularization/014 MSE recap_en.vtt 6.3 kB15 - Model Deployment/006 Streamlit functions_en.vtt 6.2 kB02 - Basic python + Pandas + Plotting/023 Subplots_en.vtt 6.1 kB12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/002 Coco Dataset + Augmentations for Segmentation with Torchvision_en.vtt 6.1 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/010 Intro_en.vtt 6.1 kB11 - Deep Learning Transfer Learning with PyTorch Lightning/009 Data Augmentation with Torchvision Transforms_en.vtt 6.0 kB04 - Machine Learning Classification + Time Series + Model Diagnostics/014 Theory behind FB Prophet_en.vtt 6.0 kB06 - 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