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Udemy Machine Learning Deep Learning and Bayesian Learning

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Uploader:

CourseClub

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Type:

Tutorials

Language:

English

Category:

Other

Size:

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Files:

[CourseClub.ME].url 122 bytes
[GigaCourse.Com].url 49 bytes
001 Introduction.mp4 41.8 MB
001 Introduction_en.vtt 2.2 KB
002 How to tackle this course.mp4 48.9 MB
002 How to tackle this course_en.vtt 6.2 KB
003 Installations and sign ups.mp4 42.8 MB
003 Installations and sign ups_en.vtt 4.8 KB
004 Jupyter Notebooks.mp4 8.7 MB
004 Jupyter Notebooks_en.vtt 4.9 KB
005 Course Material.html 130 bytes
30889860-course-code-material.zip 26.2 MB
001 Intro.mp4 2.9 MB
001 Intro_en.vtt 865 bytes
002 Basic Data Structures.mp4 21.9 MB
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003 Dictionaries.mp4 18.8 MB
003 Dictionaries_en.vtt 3.8 KB
004 Python functions (methods).mp4 27.6 MB
004 Python functions (methods)_en.vtt 5.6 KB
005 Numpy functions.mp4 62.4 MB
005 Numpy functions_en.vtt 10.6 KB
006 Conditional statements.mp4 12.6 MB
006 Conditional statements_en.vtt 3.9 KB
007 For loops.mp4 12.4 MB
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008 Dictionaries again.mp4 6.2 MB
008 Dictionaries again_en.vtt 3.1 KB
009 -------------------------------- Pandas --------------------------------.html 61 bytes
010 Intro.mp4 5.0 MB
010 Intro_en.vtt 2.4 KB
011 Pandas simple functions.mp4 38.3 MB
011 Pandas simple functions_en.vtt 11.4 KB
012 Pandas Subsetting.mp4 22.0 MB
012 Pandas Subsetting_en.vtt 6.3 KB
013 Pandas loc and iloc.mp4 41.8 MB
013 Pandas loc and iloc_en.vtt 7.6 KB
014 Pandas loc and iloc 2.mp4 13.8 MB
014 Pandas loc and iloc 2_en.vtt 5.2 KB
015 Pandas map and apply.mp4 31.4 MB
015 Pandas map and apply_en.vtt 8.2 KB
016 Pandas groupby.mp4 18.3 MB
016 Pandas groupby_en.vtt 7.0 KB
017 ----- Plotting --------.html 47 bytes
018 Plotting resources (notebooks).html 92 bytes
019 Line plot.mp4 8.6 MB
019 Line plot_en.vtt 3.2 KB
020 Plot multiple lines.mp4 45.4 MB
020 Plot multiple lines_en.vtt 3.9 KB
021 Histograms.mp4 21.6 MB
021 Histograms_en.vtt 7.9 KB
022 Scatter Plots.mp4 18.6 MB
022 Scatter Plots_en.vtt 6.4 KB
023 Subplots.mp4 15.3 MB
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024 Seaborn + pair plots.mp4 49.7 MB
024 Seaborn + pair plots_en.vtt 7.9 KB
31237618-03-0-plotting.zip 2.8 MB
31283222-multi-plot.py 440 bytes
34142844-04-pairplots.ipynb 200.5 KB
001 Your reviews are important to me!.mp4 2.0 MB
002 ----------- Numpy -------------.html 129 bytes
003 Gradient Descent.mp4 43.4 MB
003 Gradient Descent_en.vtt 16.6 KB
004 Kmeans part 1.mp4 78.4 MB
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006 Broadcasting.mp4 27.1 MB
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007 ---------------- Scikit Learn -------------------------------------.html 72 bytes
008 Intro.mp4 35.4 MB
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009 Linear Regresson Part 1.mp4 90.5 MB
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010 Linear Regression Part 2.mp4 71.6 MB
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011 Classification and Regression Trees.mp4 20.0 MB
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012 CART part 2.mp4 166.5 MB
012 CART part 2_en.vtt 20.5 KB
013 Random Forest theory.mp4 4.8 MB
013 Random Forest theory_en.vtt 2.5 KB
014 Random Forest Code.mp4 36.7 MB
014 Random Forest Code_en.vtt 6.7 KB
015 Gradient Boosted Machines.mp4 67.6 MB
015 Gradient Boosted Machines_en.vtt 9.7 KB
[CourseClub.Me].url 122 bytes
[GigaCourse.Com].url 49 bytes
001 Kaggle part 1.mp4 6.7 MB
001 Kaggle part 1_en.vtt 2.6 KB
002 Kaggle part 2.mp4 11.1 MB
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003 Theory part 1.mp4 13.5 MB
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004 Theory part 2 + code.mp4 27.3 MB
004 Theory part 2 + code_en.vtt 6.3 KB
005 Titanic dataset.mp4 116.3 MB
005 Titanic dataset_en.vtt 15.2 KB
006 Sklearn classification prelude.mp4 14.3 MB
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007 Sklearn classification.mp4 90.0 MB
007 Sklearn classification_en.vtt 14.5 KB
008 Dealing with missing values.mp4 50.8 MB
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009 --------- Time Series -------------------.html 255 bytes
010 Intro.mp4 11.4 MB
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011 Loss functions.mp4 46.4 MB
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012 FB Prophet part 1.mp4 78.0 MB
012 FB Prophet part 1_en.vtt 9.8 KB
013 FB Prophet part 2.mp4 24.5 MB
013 FB Prophet part 2_en.vtt 4.1 KB
014 Theory behind FB Prophet.mp4 16.9 MB
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015 ------------ Model Diagnostics -----.html 112 bytes
016 Overfitting.mp4 19.3 MB
016 Overfitting_en.vtt 7.0 KB
017 Cross Validation.mp4 53.7 MB
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018 Stratified K Fold.mp4 58.1 MB
018 Stratified K Fold_en.vtt 9.9 KB
019 Area Under Curve (AUC) Part 1.mp4 84.1 MB
019 Area Under Curve (AUC) Part 1_en.vtt 9.2 KB
020 Area Under Curve (AUC) Part 2.mp4 19.5 MB
020 Area Under Curve (AUC) Part 2_en.vtt 7.0 KB
001 Principal Component Analysis (PCA) theory.mp4 20.5 MB
001 Principal Component Analysis (PCA) theory_en.vtt 9.0 KB
002 Fashion MNIST PCA.mp4 102.1 MB
002 Fashion MNIST PCA_en.vtt 10.5 KB
003 K-means.mp4 22.3 MB
003 K-means_en.vtt 7.6 KB
004 Other clustering methods.mp4 48.1 MB
004 Other clustering methods_en.vtt 7.2 KB
005 DBSCAN theory.mp4 13.2 MB
005 DBSCAN theory_en.vtt 6.9 KB
006 Gaussian Mixture Models (GMM) theory.mp4 20.0 MB
006 Gaussian Mixture Models (GMM) theory_en.vtt 7.9 KB
001 Intro.mp4 10.4 MB
001 Intro_en.vtt 5.4 KB
002 Stop words and Term Frequency.mp4 10.7 MB
002 Stop words and Term Frequency_en.vtt 4.9 KB
003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory.mp4 6.1 MB
003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory_en.vtt 3.0 KB
004 Financial News Sentiment Classifier.mp4 33.7 MB
004 Financial News Sentiment Classifier_en.vtt 10.0 KB
005 NLTK + Stemming.mp4 45.6 MB
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006 N-grams.mp4 13.8 MB
006 N-grams_en.vtt 4.0 KB
007 Word (feature) importance.mp4 12.4 MB
007 Word (feature) importance_en.vtt 3.8 KB
008 Spacy intro.mp4 33.2 MB
008 Spacy intro_en.vtt 5.6 KB
009 Feature Extraction with Spacy (using Pandas).mp4 76.5 MB
009 Feature Extraction with Spacy (using Pandas)_en.vtt 9.8 KB
010 Classification Example.mp4 24.1 MB
010 Classification Example_en.vtt 4.3 KB
011 Over-sampling.mp4 32.8 MB
011 Over-sampling_en.vtt 5.8 KB
012 -------- Regularization ------------.html 218 bytes
013 Introduction.mp4 8.4 MB
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014 MSE recap.mp4 18.3 MB
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015 L2 Loss Ridge Regression intro.mp4 10.0 MB
015 L2 Loss Ridge Regression intro_en.vtt 3.6 KB
016 Ridge regression (L2 penalised regression).mp4 47.0 MB
016 Ridge regression (L2 penalised regression)_en.vtt 7.9 KB
017 S&P500 data preparation for L1 loss.mp4 25.2 MB
017 S&P500 data preparation for L1 loss_en.vtt 7.1 KB
018 L1 Penalised Regression (Lasso).mp4 31.4 MB
018 L1 Penalised Regression (Lasso)_en.vtt 5.6 KB
019 L1 L2 Penalty theory why it works.mp4 23.2 MB
019 L1 L2 Penalty theory why it works_en.vtt 3.8 KB
31762302-06-0-reguralisation.zip 2.6 MB
001 Intro.mp4 632.6 KB
001 Intro_en.vtt 473 bytes
002 DL theory part 1.mp4 17.2 MB
002 DL theory part 1_en.vtt 6.1 KB
003 DL theory part 2.mp4 22.8 MB
003 DL theory part 2_en.vtt 3.9 KB
004 Tensorflow + Keras demo problem 1.mp4 43.3 MB
004 Tensorflow + Keras demo problem 1_en.vtt 16.4 KB
005 Activation functions.mp4 15.4 MB
005 Activation functions_en.vtt 5.5 KB
006 First example with Relu.mp4 32.6 MB
006 First example with Relu_en.vtt 5.4 KB
007 MNIST and Softmax.mp4 55.8 MB
007 MNIST and Softmax_en.vtt 10.4 KB
008 Deep Learning Input Normalisation.mp4 10.3 MB
008 Deep Learning Input Normalisation_en.vtt 3.2 KB
009 Softmax theory.mp4 58.3 MB
009 Softmax theory_en.vtt 5.5 KB
010 Batch Norm.mp4 17.0 MB
010 Batch Norm_en.vtt 5.7 KB
011 Batch Norm Theory.mp4 53.9 MB
011 Batch Norm Theory_en.vtt 8.3 KB
32725408-09-tensorflow.zip 2.7 MB
[CourseClub.Me].url 122 bytes
[GigaCourse.Com].url 49 bytes
001 Intro.mp4 6.0 MB
001 Intro_en.vtt 3.2 KB
002 Fashion MNIST feed forward net for benchmarking.mp4 19.7 MB
002 Fashion MNIST feed forward net for benchmarking_en.vtt 3.5 KB
003 Keras Conv2D layer.mp4 44.5 MB
003 Keras Conv2D layer_en.vtt 8.6 KB
004 Model fitting and discussion of results.mp4 17.4 MB
004 Model fitting and discussion of results_en.vtt 2.9 KB
005 Dropout theory and code.mp4 23.7 MB
005 Dropout theory and code_en.vtt 7.0 KB
006 MaxPool (and comparison to stride).mp4 17.7 MB
006 MaxPool (and comparison to stride)_en.vtt 5.4 KB
007 Cifar-10.mp4 27.3 MB
007 Cifar-10_en.vtt 10.1 KB
008 Nose Tip detection with CNNs.mp4 68.7 MB
008 Nose Tip detection with CNNs_en.vtt 12.5 KB
001 Word2vec and Embeddings.mp4 44.0 MB
001 Word2vec and Embeddings_en.vtt 8.3 KB
002 Kaggle + Word2Vec.mp4 27.8 MB
002 Kaggle + Word2Vec_en.vtt 10.5 KB
003 Word2Vec keras Model API.mp4 45.2 MB
003 Word2Vec keras Model API_en.vtt 13.3 KB
004 Recurrent Neural Nets - Theory.mp4 19.1 MB
004 Recurrent Neural Nets - Theory_en.vtt 10.6 KB
005 Deep Learning - Long Short Term Memory (LSTM) Nets.mp4 91.0 MB
005 Deep Learning - Long Short Term Memory (LSTM) Nets_en.vtt 11.8 KB
006 Deep Learning - Stacking LSTMs + GRUs.mp4 5.0 MB
006 Deep Learning - Stacking LSTMs + GRUs_en.vtt 2.2 KB
007 Transfer Learning - GLOVE vectors.mp4 74.6 MB
007 Transfer Learning - GLOVE vectors_en.vtt 11.4 KB
008 Sequence to Sequence Introduction + Data Prep.mp4 80.1 MB
008 Sequence to Sequence Introduction + Data Prep_en.vtt 8.0 KB
009 Sequence to Sequence model + Keras Model API.mp4 30.5 MB
009 Sequence to Sequence model + Keras Model API_en.vtt 8.7 KB
010 Sequence to Sequence models Prediction step.mp4 104.7 MB
010 Sequence to Sequence models Prediction step_en.vtt 13.1 KB
001 Introduction.mp4 2.2 MB
001 Introduction_en.vtt 1.2 KB
002 Pytorch TensorDataset.mp4 12.4 MB
002 Pytorch TensorDataset_en.vtt 5.0 KB
003 Pytorch Dataset and DataLoaders.mp4 35.4 MB
003 Pytorch Dataset and DataLoaders_en.vtt 5.7 KB
004 Deep Learning with PyTorch nn.Sequential models.mp4 11.0 MB
004 Deep Learning with PyTorch nn.Sequential models_en.vtt 5.7 KB
005 Deep Learning with Pytorch Loss functions.mp4 52.4 MB
005 Deep Learning with Pytorch Loss functions_en.vtt 8.7 KB
006 Deep Learning with Pytorch Stochastic Gradient Descent.mp4 79.5 MB
006 Deep Learning with Pytorch Stochastic Gradient Descent_en.vtt 8.1 KB
007 Deep Learning with Pytorch Optimizers.mp4 10.2 MB
007 Deep Learning with Pytorch Optimizers_en.vtt 3.4 KB
008 Pytorch Model API.mp4 33.2 MB
008 Pytorch Model API_en.vtt 5.5 KB
009 Pytorch in GPUs.mp4 5.0 MB
009 Pytorch in GPUs_en.vtt 2.6 KB
010 Deep Learning Intro to Pytorch Lightning.mp4 52.4 MB
010 Deep Learning Intro to Pytorch Lightning_en.vtt 9.3 KB
external-assets-links.txt 122 bytes
001 Transfer Learning Introduction.mp4 4.5 MB
001 Transfer Learning Introduction_en.vtt 2.0 KB
002 Kaggle problem description.mp4 9.2 MB
002 Kaggle problem description_en.vtt 2.8 KB
003 PyTorch datasets + Torchvision.mp4 14.7 MB
003 PyTorch datasets + Torchvision_en.vtt 4.2 KB
004 PyTorch transfer learning with ResNet.mp4 15.4 MB
004 PyTorch transfer learning with ResNet_en.vtt 4.4 KB
005 PyTorch Lightning Model.mp4 9.4 MB
005 PyTorch Lightning Model_en.vtt 3.9 KB
006 PyTorch Lightning Trainer + Model evaluation.mp4 50.2 MB
006 PyTorch Lightning Trainer + Model evaluation_en.vtt 6.3 KB
007 Deep Learning for Cassava Leaf Classification.mp4 4.1 MB
007 Deep Learning for Cassava Leaf Classification_en.vtt 1.1 KB
008 Cassava Leaf Dataset.mp4 15.3 MB
008 Cassava Leaf Dataset_en.vtt 4.8 KB
009 Data Augmentation with Torchvision Transforms.mp4 56.5 MB
009 Data Augmentation with Torchvision Transforms_en.vtt 5.9 KB
010 Train vs Test Augmentations + DataLoader parameters.mp4 7.7 MB
010 Train vs Test Augmentations + DataLoader parameters_en.vtt 3.3 KB
011 Deep Learning Transfer Learning Model with ResNet.mp4 8.0 MB
011 Deep Learning Transfer Learning Model with ResNet_en.vtt 3.3 KB
012 Setting up PyTorch Lightning for training.mp4 8.4 MB
012 Setting up PyTorch Lightning for training_en.vtt 3.5 KB
013 Cross Entropy Loss for Imbalanced Classes.mp4 8.5 MB
013 Cross Entropy Loss for Imbalanced Classes_en.vtt 3.9 KB
014 PyTorch Test dataset setup and evaluation.mp4 7.1 MB
014 PyTorch Test dataset setup and evaluation_en.vtt 2.9 KB
015 WandB for logging experiments.mp4 21.5 MB
015 WandB for logging experiments_en.vtt 5.4 KB
[CourseClub.Me].url 122 bytes
[GigaCourse.Com].url 49 bytes
001 Introduction.mp4 25.3 MB
001 Introduction_en.vtt 2.6 KB
002 Coco Dataset + Augmentations for Segmentation with Torchvision.mp4 18.9 MB
002 Coco Dataset + Augmentations for Segmentation with Torchvision_en.vtt 5.9 KB
003 Unet Architecture overview.mp4 14.7 MB
003 Unet Architecture overview_en.vtt 6.4 KB
004 PyTorch Model Architecture.mp4 13.5 MB
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005 PyTorch Hooks.mp4 24.7 MB
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006 PyTorch Hooks Step through with breakpoints.mp4 67.6 MB
006 PyTorch Hooks Step through with breakpoints_en.vtt 8.8 KB
007 PyTorch Weighted CrossEntropy Loss.mp4 65.2 MB
007 PyTorch Weighted CrossEntropy Loss_en.vtt 9.1 KB
008 Weights and Biases Logging images.mp4 15.8 MB
008 Weights and Biases Logging images_en.vtt 1.9 KB
009 Semantic Segmentation training with PyTorch Lightning.mp4 130.2 MB
009 Semantic Segmentation training with PyTorch Lightning_en.vtt 16.2 KB
external-assets-links.txt 52 bytes
001 Introduction to Transformers.mp4 3.4 MB
001 Introduction to Transformers_en.vtt 1.6 KB
002 The illustrated Transformer (blogpost by Jay Alammar).mp4 23.6 MB
002 The illustrated Transformer (blogpost by Jay Alammar)_en.vtt 8.9 KB
003 Encoder Transformer Models The Maths.mp4 28.7 MB
003 Encoder Transformer Models The Maths_en.vtt 5.6 KB
004 BERT - The theory.mp4 8.1 MB
004 BERT - The theory_en.vtt 3.8 KB
005 Kaggle Multi-lingual Toxic Comment Classification Challenge.mp4 6.8 MB
005 Kaggle Multi-lingual Toxic Comment Classification Challenge_en.vtt 2.0 KB
006 Tokenizers and data prep for BERT models.mp4 29.1 MB
006 Tokenizers and data prep for BERT models_en.vtt 10.8 KB
007 Distilbert (Smaller BERT) model.mp4 48.8 MB
007 Distilbert (Smaller BERT) model_en.vtt 10.8 KB
008 Pytorch Lightning + DistilBERT for classification.mp4 102.7 MB
008 Pytorch Lightning + DistilBERT for classification_en.vtt 17.3 KB
external-assets-links.txt 264 bytes
001 Introduction and Terminology.mp4 18.1 MB
001 Introduction and Terminology_en.vtt 8.3 KB
002 Bayesian Learning Distributions.mp4 35.9 MB
002 Bayesian Learning Distributions_en.vtt 10.5 KB
003 Bayes rule for population mean estimation.mp4 50.2 MB
003 Bayes rule for population mean estimation_en.vtt 9.0 KB
004 Bayesian learning Population estimation pymc3 way.mp4 70.6 MB
004 Bayesian learning Population estimation pymc3 way_en.vtt 8.9 KB
005 Coin Toss Example with Pymc3.mp4 70.7 MB
005 Coin Toss Example with Pymc3_en.vtt 8.0 KB
006 Data Setup for Bayesian Linear Regression.mp4 17.1 MB
006 Data Setup for Bayesian Linear Regression_en.vtt 4.7 KB
007 Bayesian Linear Regression with pymc3.mp4 60.1 MB
007 Bayesian Linear Regression with pymc3_en.vtt 10.0 KB
008 Bayesian Rolling Regression - Problem setup.mp4 14.8 MB
008 Bayesian Rolling Regression - Problem setup_en.vtt 5.6 KB
009 Bayesian Rolling regression - pymc3 way.mp4 54.8 MB
009 Bayesian Rolling regression - pymc3 way_en.vtt 9.3 KB
010 Bayesian Rolling Regression - forecasting.mp4 30.3 MB
010 Bayesian Rolling Regression - forecasting_en.vtt 5.3 KB
011 Variational Bayes Intro.mp4 8.6 MB
011 Variational Bayes Intro_en.vtt 3.2 KB
012 Variational Bayes Linear Classification.mp4 44.3 MB
012 Variational Bayes Linear Classification_en.vtt 7.5 KB
013 Variational Bayesian Inference Result Analysis.mp4 7.4 MB
013 Variational Bayesian Inference Result Analysis_en.vtt 3.8 KB
014 Minibatch Variational Bayes.mp4 11.0 MB
014 Minibatch Variational Bayes_en.vtt 3.9 KB
015 Deep Bayesian Networks.mp4 7.3 MB
015 Deep Bayesian Networks_en.vtt 3.2 KB
016 Deep Bayesian Networks - analysis.mp4 10.5 MB
016 Deep Bayesian Networks - analysis_en.vtt 4.1 KB
31919076-bayesian-inference.zip 1.8 MB
001 Intro.mp4 2.5 MB
001 Intro_en.vtt 1.2 KB
002 Saving Models.mp4 7.6 MB
002 Saving Models_en.vtt 3.1 KB
003 FastAPI intro.mp4 11.6 MB
003 FastAPI intro_en.vtt 5.3 KB
004 FastAPI serving model.mp4 29.3 MB
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005 Streamlit Intro.mp4 6.0 MB
005 Streamlit Intro_en.vtt 2.6 KB
006 Streamlit functions.mp4 20.8 MB
006 Streamlit functions_en.vtt 6.1 KB
007 CLIP model.mp4 18.7 MB
007 CLIP model_en.vtt 7.3 KB
[CourseClub.Me].url 122 bytes
[GigaCourse.Com].url 49 bytes
001 Some advice on your journey.mp4 13.6 MB
001 Some advice on your journey_en.vtt 3.8 KB
[CourseClub.Me].url 122 bytes
[GigaCourse.Com].url 49 bytes

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