Udemy Machine Learning Deep Learning and Bayesian Learning
Torrent Info
| Category | Other |
| Added | Tuesday 24th October 2023 01:27:08 AM (3 years ago) |
| Size | 5.5 GB |
| Peers | 0 Peers (0 Seeders and 0 leechers) update |
| Downloaded | 0 times |
| Uploader | CourseClub Thank You (0) Report Torrent 00/0 |
| Comments | None |
| Uploader Comments | None |
| Tags | English |
| Seeders | 0 |
| Leechers | 0 |
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| Filename | Size |
|---|---|
| [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 |
| 002 Basic Data Structures_en.vtt | 6.4 KB |
| 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 |
| 007 For loops_en.vtt | 4.2 KB |
| 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 |
| 023 Subplots_en.vtt | 6.0 KB |
| 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 |
| 004 Kmeans part 1_en.vtt | 11.8 KB |
| 005 Kmeans part 2.mp4 | 63.2 MB |
| 005 Kmeans part 2_en.vtt | 19.7 KB |
| 006 Broadcasting.mp4 | 27.1 MB |
| 006 Broadcasting_en.vtt | 9.6 KB |
| 007 ---------------- Scikit Learn -------------------------------------.html | 72 bytes |
| 008 Intro.mp4 | 35.4 MB |
| 008 Intro_en.vtt | 4.9 KB |
| 009 Linear Regresson Part 1.mp4 | 90.5 MB |
| 009 Linear Regresson Part 1_en.vtt | 12.2 KB |
| 010 Linear Regression Part 2.mp4 | 71.6 MB |
| 010 Linear Regression Part 2_en.vtt | 11.2 KB |
| 011 Classification and Regression Trees.mp4 | 20.0 MB |
| 011 Classification and Regression Trees_en.vtt | 6.4 KB |
| 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 |
| 002 Kaggle part 2_en.vtt | 3.3 KB |
| 003 Theory part 1.mp4 | 13.5 MB |
| 003 Theory part 1_en.vtt | 6.7 KB |
| 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 |
| 006 Sklearn classification prelude_en.vtt | 5.3 KB |
| 007 Sklearn classification.mp4 | 90.0 MB |
| 007 Sklearn classification_en.vtt | 14.5 KB |
| 008 Dealing with missing values.mp4 | 50.8 MB |
| 008 Dealing with missing values_en.vtt | 5.8 KB |
| 009 --------- Time Series -------------------.html | 255 bytes |
| 010 Intro.mp4 | 11.4 MB |
| 010 Intro_en.vtt | 5.9 KB |
| 011 Loss functions.mp4 | 46.4 MB |
| 011 Loss functions_en.vtt | 7.2 KB |
| 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 |
| 014 Theory behind FB Prophet_en.vtt | 5.9 KB |
| 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 |
| 017 Cross Validation_en.vtt | 8.3 KB |
| 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 |
| 005 NLTK + Stemming_en.vtt | 7.8 KB |
| 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 |
| 013 Introduction_en.vtt | 2.6 KB |
| 014 MSE recap.mp4 | 18.3 MB |
| 014 MSE recap_en.vtt | 6.1 KB |
| 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 |
| 004 PyTorch Model Architecture_en.vtt | 3.6 KB |
| 005 PyTorch Hooks.mp4 | 24.7 MB |
| 005 PyTorch Hooks_en.vtt | 7.3 KB |
| 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 |
| 004 FastAPI serving model_en.vtt | 7.5 KB |
| 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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2026-03-02 09:32:04 |
0 |
1.3 GB |
0 |
5 |
0 |
|
|
freecoursewb in
Other
::
Other
|
|
2026-03-02 09:06:03 |
0 |
2.2 GB |
0 |
4 |
0 |
|
|
tutsgalaxy in
Other
::
Other
|
|
2023-06-02 12:28:09 |
0 |
3.4 GB |
0 |
3 |
0 |
|
|
tutsgalaxy in
Other
::
Other
|
|
2023-06-02 11:31:29 |
0 |
5.1 GB |
0 |
3 |
0 |
|
|
2023-10-22 07:08:46 |
0 |
8.6 GB |
0 |
2 |
0 |
||
|
2023-08-05 17:43:43 |
0 |
10.5 GB |
0 |
2 |
2 |
||
|
2023-06-02 00:26:27 |
0 |
1.2 GB |
0 |
2 |
0 |
||