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Machine Learning, Deep Learning and Bayesian Learning
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[TutsNode.com] - Machine Learning, Deep Learning and Bayesian Learning (Size: 5.63 GB) (Files: 548)
[TutsNode.com] - Machine Learning, Deep Learning and Bayesian Learning
03 - Machine Learning Numpy + Scikit Learn
012 CART part 2.mp4
012 CART part 2_en.vtt
005 Kmeans part 2_en.vtt
003 Gradient Descent_en.vtt
009 Linear Regresson Part 1_en.vtt
004 Kmeans part 1_en.vtt
010 Linear Regression Part 2_en.vtt
015 Gradient Boosted Machines_en.vtt
006 Broadcasting_en.vtt
002 ----------- Numpy -------------.html
007 ---------------- Scikit Learn -------------------------------------.html
013 Random Forest theory_en.vtt
014 Random Forest Code_en.vtt
011 Classification and Regression Trees_en.vtt
008 Intro_en.vtt
009 Linear Regresson Part 1.mp4
004 Kmeans part 1.mp4
010 Linear Regression Part 2.mp4
015 Gradient Boosted Machines.mp4
005 Kmeans part 2.mp4
003 Gradient Descent.mp4
014 Random Forest Code.mp4
008 Intro.mp4
006 Broadcasting.mp4
011 Classification and Regression Trees.mp4
013 Random Forest theory.mp4
001 Your reviews are important to me!.mp4
02 - Basic python + Pandas + Plotting
34142844-04-pairplots.ipynb
001 Intro_en.vtt
011 Pandas simple functions_en.vtt
005 Numpy functions_en.vtt
009 -------------------------------- Pandas --------------------------------.html
010 Intro_en.vtt
017 ----- Plotting --------.html
018 Plotting resources (notebooks).html
31283222-multi-plot.py
015 Pandas map and apply_en.vtt
024 Seaborn + pair plots_en.vtt
021 Histograms_en.vtt
013 Pandas loc and iloc_en.vtt
016 Pandas groupby_en.vtt
002 Basic Data Structures_en.vtt
022 Scatter Plots_en.vtt
012 Pandas Subsetting_en.vtt
023 Subplots_en.vtt
004 Python functions (methods)_en.vtt
014 Pandas loc and iloc 2_en.vtt
007 For loops_en.vtt
006 Conditional statements_en.vtt
020 Plot multiple lines_en.vtt
003 Dictionaries_en.vtt
019 Line plot_en.vtt
008 Dictionaries again_en.vtt
005 Numpy functions.mp4
024 Seaborn + pair plots.mp4
020 Plot multiple lines.mp4
013 Pandas loc and iloc.mp4
011 Pandas simple functions.mp4
015 Pandas map and apply.mp4
004 Python functions (methods).mp4
012 Pandas Subsetting.mp4
002 Basic Data Structures.mp4
021 Histograms.mp4
003 Dictionaries.mp4
022 Scatter Plots.mp4
016 Pandas groupby.mp4
023 Subplots.mp4
014 Pandas loc and iloc 2.mp4
006 Conditional statements.mp4
007 For loops.mp4
019 Line plot.mp4
008 Dictionaries again.mp4
010 Intro.mp4
001 Intro.mp4
31237618-03-0-plotting.zip
01 - Introduction
001 Introduction_en.vtt
005 Course Material.html
002 How to tackle this course_en.vtt
004 Jupyter Notebooks_en.vtt
003 Installations and sign ups_en.vtt
002 How to tackle this course.mp4
003 Installations and sign ups.mp4
001 Introduction.mp4
30889860-course-code-material.zip
004 Jupyter Notebooks.mp4
13 - Deep Learning Transformers and BERT
008 Pytorch Lightning + DistilBERT for classification_en.vtt
006 Tokenizers and data prep for BERT models_en.vtt
007 Distilbert (Smaller BERT) model_en.vtt
002 The illustrated Transformer (blogpost by Jay Alammar)_en.vtt
003 Encoder Transformer Models The Maths_en.vtt
004 BERT - The theory_en.vtt
005 Kaggle Multi-lingual Toxic Comment Classification Challenge_en.vtt
001 Introduction to Transformers_en.vtt
external-assets-links.txt
008 Pytorch Lightning + DistilBERT for classification.mp4
007 Distilbert (Smaller BERT) model.mp4
006 Tokenizers and data prep for BERT models.mp4
003 Encoder Transformer Models The Maths.mp4
002 The illustrated Transformer (blogpost by Jay Alammar).mp4
004 BERT - The theory.mp4
005 Kaggle Multi-lingual Toxic Comment Classification Challenge.mp4
001 Introduction to Transformers.mp4
07 - Deep Learning
004 Tensorflow + Keras demo problem 1_en.vtt
001 Intro.mp4
007 MNIST and Softmax_en.vtt
011 Batch Norm Theory_en.vtt
002 DL theory part 1_en.vtt
010 Batch Norm_en.vtt
009 Softmax theory_en.vtt
005 Activation functions_en.vtt
006 First example with Relu_en.vtt
003 DL theory part 2_en.vtt
008 Deep Learning Input Normalisation_en.vtt
001 Intro_en.vtt
009 Softmax theory.mp4
007 MNIST and Softmax.mp4
011 Batch Norm Theory.mp4
004 Tensorflow + Keras demo problem 1.mp4
006 First example with Relu.mp4
003 DL theory part 2.mp4
002 DL theory part 1.mp4
010 Batch Norm.mp4
005 Activation functions.mp4
008 Deep Learning Input Normalisation.mp4
32725408-09-tensorflow.zip
12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch
009 Semantic Segmentation training with PyTorch Lightning_en.vtt
009 Semantic Segmentation training with PyTorch Lightning.mp4
007 PyTorch Weighted CrossEntropy Loss_en.vtt
006 PyTorch Hooks Step through with breakpoints_en.vtt
005 PyTorch Hooks_en.vtt
003 Unet Architecture overview_en.vtt
002 Coco Dataset + Augmentations for Segmentation with Torchvision_en.vtt
004 PyTorch Model Architecture_en.vtt
001 Introduction_en.vtt
008 Weights and Biases Logging images_en.vtt
external-assets-links.txt
006 PyTorch Hooks Step through with breakpoints.mp4
007 PyTorch Weighted CrossEntropy Loss.mp4
001 Introduction.mp4
005 PyTorch Hooks.mp4
002 Coco Dataset + Augmentations for Segmentation with Torchvision.mp4
008 Weights and Biases Logging images.mp4
003 Unet Architecture overview.mp4
004 PyTorch Model Architecture.mp4
04 - Machine Learning Classification + Time Series + Model Diagnostics
005 Titanic dataset_en.vtt
007 Sklearn classification_en.vtt
018 Stratified K Fold_en.vtt
012 FB Prophet part 1_en.vtt
019 Area Under Curve (AUC) Part 1_en.vtt
017 Cross Validation_en.vtt
011 Loss functions_en.vtt
001 Kaggle part 1_en.vtt
016 Overfitting_en.vtt
020 Area Under Curve (AUC) Part 2_en.vtt
003 Theory part 1_en.vtt
004 Theory part 2 + code_en.vtt
009 --------- Time Series -------------------.html
010 Intro_en.vtt
014 Theory behind FB Prophet_en.vtt
008 Dealing with missing values_en.vtt
015 ------------ Model Diagnostics -----.html
006 Sklearn classification prelude_en.vtt
013 FB Prophet part 2_en.vtt
002 Kaggle part 2_en.vtt
005 Titanic dataset.mp4
007 Sklearn classification.mp4
019 Area Under Curve (AUC) Part 1.mp4
012 FB Prophet part 1.mp4
018 Stratified K Fold.mp4
017 Cross Validation.mp4
008 Dealing with missing values.mp4
011 Loss functions.mp4
004 Theory part 2 + code.mp4
013 FB Prophet part 2.mp4
020 Area Under Curve (AUC) Part 2.mp4
016 Overfitting.mp4
014 Theory behind FB Prophet.mp4
006 Sklearn classification prelude.mp4
003 Theory part 1.mp4
010 Intro.mp4
002 Kaggle part 2.mp4
001 Kaggle part 1.mp4
09 - Deep Learning Recurrent Neural Nets
003 Word2Vec keras Model API_en.vtt
010 Sequence to Sequence models Prediction step_en.vtt
005 Deep Learning - Long Short Term Memory (LSTM) Nets_en.vtt
007 Transfer Learning - GLOVE vectors_en.vtt
004 Recurrent Neural Nets - Theory_en.vtt
002 Kaggle + Word2Vec_en.vtt
009 Sequence to Sequence model + Keras Model API_en.vtt
001 Word2vec and Embeddings_en.vtt
008 Sequence to Sequence Introduction + Data Prep_en.vtt
006 Deep Learning - Stacking LSTMs + GRUs_en.vtt
010 Sequence to Sequence models Prediction step.mp4
005 Deep Learning - Long Short Term Memory (LSTM) Nets.mp4
008 Sequence to Sequence Introduction + Data Prep.mp4
007 Transfer Learning - GLOVE vectors.mp4
003 Word2Vec keras Model API.mp4
001 Word2vec and Embeddings.mp4
009 Sequence to Sequence model + Keras Model API.mp4
002 Kaggle + Word2Vec.mp4
004 Recurrent Neural Nets - Theory.mp4
006 Deep Learning - Stacking LSTMs + GRUs.mp4
08 - Deep Learning (TensorFlow) - Convolutional Neural Nets
008 Nose Tip detection with CNNs_en.vtt
007 Cifar-10_en.vtt
003 Keras Conv2D layer_en.vtt
005 Dropout theory and code_en.vtt
006 MaxPool (and comparison to stride)_en.vtt
002 Fashion MNIST feed forward net for benchmarking_en.vtt
001 Intro_en.vtt
004 Model fitting and discussion of results_en.vtt
008 Nose Tip detection with CNNs.mp4
003 Keras Conv2D layer.mp4
007 Cifar-10.mp4
005 Dropout theory and code.mp4
002 Fashion MNIST feed forward net for benchmarking.mp4
006 MaxPool (and comparison to stride).mp4
004 Model fitting and discussion of results.mp4
001 Intro.mp4
05 - Unsupervised Learning
002 Fashion MNIST PCA_en.vtt
001 Principal Component Analysis (PCA) theory_en.vtt
006 Gaussian Mixture Models (GMM) theory_en.vtt
003 K-means_en.vtt
004 Other clustering methods_en.vtt
005 DBSCAN theory_en.vtt
002 Fashion MNIST PCA.mp4
004 Other clustering methods.mp4
003 K-means.mp4
001 Principal Component Analysis (PCA) theory.mp4
006 Gaussian Mixture Models (GMM) theory.mp4
005 DBSCAN theory.mp4
11 - Deep Learning Transfer Learning with PyTorch Lightning
010 Train vs Test Augmentations + DataLoader parameters_en.vtt
006 PyTorch Lightning Trainer + Model evaluation_en.vtt
009 Data Augmentation with Torchvision Transforms_en.vtt
015 WandB for logging experiments_en.vtt
008 Cassava Leaf Dataset_en.vtt
004 PyTorch transfer learning with ResNet_en.vtt
003 PyTorch datasets + Torchvision_en.vtt
013 Cross Entropy Loss for Imbalanced Classes_en.vtt
005 PyTorch Lightning Model_en.vtt
012 Setting up PyTorch Lightning for training_en.vtt
011 Deep Learning Transfer Learning Model with ResNet_en.vtt
014 PyTorch Test dataset setup and evaluation_en.vtt
002 Kaggle problem description_en.vtt
001 Transfer Learning Introduction_en.vtt
007 Deep Learning for Cassava Leaf Classification_en.vtt
009 Data Augmentation with Torchvision Transforms.mp4
006 PyTorch Lightning Trainer + Model evaluation.mp4
015 WandB for logging experiments.mp4
004 PyTorch transfer learning with ResNet.mp4
008 Cassava Leaf Dataset.mp4
003 PyTorch datasets + Torchvision.mp4
005 PyTorch Lightning Model.mp4
002 Kaggle problem description.mp4
013 Cross Entropy Loss for Imbalanced Classes.mp4
012 Setting up PyTorch Lightning for training.mp4
011 Deep Learning Transfer Learning Model with ResNet.mp4
010 Train vs Test Augmentations + DataLoader parameters.mp4
014 PyTorch Test dataset setup and evaluation.mp4
001 Transfer Learning Introduction.mp4
007 Deep Learning for Cassava Leaf Classification.mp4
14 - Bayesian Learning and probabilistic programming
002 Bayesian Learning Distributions_en.vtt
007 Bayesian Linear Regression with pymc3_en.vtt
009 Bayesian Rolling regression - pymc3 way_en.vtt
003 Bayes rule for population mean estimation_en.vtt
004 Bayesian learning Population estimation pymc3 way_en.vtt
001 Introduction and Terminology_en.vtt
005 Coin Toss Example with Pymc3_en.vtt
012 Variational Bayes Linear Classification_en.vtt
008 Bayesian Rolling Regression - Problem setup_en.vtt
010 Bayesian Rolling Regression - forecasting_en.vtt
006 Data Setup for Bayesian Linear Regression_en.vtt
016 Deep Bayesian Networks - analysis_en.vtt
014 Minibatch Variational Bayes_en.vtt
013 Variational Bayesian Inference Result Analysis_en.vtt
011 Variational Bayes Intro_en.vtt
015 Deep Bayesian Networks_en.vtt
005 Coin Toss Example with Pymc3.mp4
004 Bayesian learning Population estimation pymc3 way.mp4
007 Bayesian Linear Regression with pymc3.mp4
009 Bayesian Rolling regression - pymc3 way.mp4
003 Bayes rule for population mean estimation.mp4
012 Variational Bayes Linear Classification.mp4
002 Bayesian Learning Distributions.mp4
010 Bayesian Rolling Regression - forecasting.mp4
001 Introduction and Terminology.mp4
006 Data Setup for Bayesian Linear Regression.mp4
008 Bayesian Rolling Regression - Problem setup.mp4
014 Minibatch Variational Bayes.mp4
016 Deep Bayesian Networks - analysis.mp4
011 Variational Bayes Intro.mp4
013 Variational Bayesian Inference Result Analysis.mp4
015 Deep Bayesian Networks.mp4
31919076-bayesian-inference.zip
06 - Natural Language Processing + Regularization
004 Financial News Sentiment Classifier_en.vtt
009 Feature Extraction with Spacy (using Pandas)_en.vtt
016 Ridge regression (L2 penalised regression)_en.vtt
005 NLTK + Stemming_en.vtt
017 S&P500 data preparation for L1 loss_en.vtt
014 MSE recap_en.vtt
011 Over-sampling_en.vtt
018 L1 Penalised Regression (Lasso)_en.vtt
008 Spacy intro_en.vtt
001 Intro_en.vtt
002 Stop words and Term Frequency_en.vtt
010 Classification Example_en.vtt
006 N-grams_en.vtt
019 L1 L2 Penalty theory why it works_en.vtt
007 Word (feature) importance_en.vtt
012 -------- Regularization ------------.html
013 Introduction_en.vtt
015 L2 Loss Ridge Regression intro_en.vtt
003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory_en.vtt
009 Feature Extraction with Spacy (using Pandas).mp4
016 Ridge regression (L2 penalised regression).mp4
005 NLTK + Stemming.mp4
004 Financial News Sentiment Classifier.mp4
008 Spacy intro.mp4
011 Over-sampling.mp4
018 L1 Penalised Regression (Lasso).mp4
017 S&P500 data preparation for L1 loss.mp4
010 Classification Example.mp4
019 L1 L2 Penalty theory why it works.mp4
014 MSE recap.mp4
006 N-grams.mp4
007 Word (feature) importance.mp4
002 Stop words and Term Frequency.mp4
001 Intro.mp4
015 L2 Loss Ridge Regression intro.mp4
013 Introduction.mp4
003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory.mp4
31762302-06-0-reguralisation.zip
10 - Deep Learning PyTorch Introduction
010 Deep Learning Intro to Pytorch Lightning_en.vtt
005 Deep Learning with Pytorch Loss functions_en.vtt
006 Deep Learning with Pytorch Stochastic Gradient Descent_en.vtt
003 Pytorch Dataset and DataLoaders_en.vtt
004 Deep Learning with PyTorch nn.Sequential models_en.vtt
008 Pytorch Model API_en.vtt
002 Pytorch TensorDataset_en.vtt
007 Deep Learning with Pytorch Optimizers_en.vtt
009 Pytorch in GPUs_en.vtt
001 Introduction_en.vtt
external-assets-links.txt
006 Deep Learning with Pytorch Stochastic Gradient Descent.mp4
005 Deep Learning with Pytorch Loss functions.mp4
010 Deep Learning Intro to Pytorch Lightning.mp4
003 Pytorch Dataset and DataLoaders.mp4
008 Pytorch Model API.mp4
002 Pytorch TensorDataset.mp4
004 Deep Learning with PyTorch nn.Sequential models.mp4
007 Deep Learning with Pytorch Optimizers.mp4
009 Pytorch in GPUs.mp4
001 Introduction.mp4
15 - Model Deployment
004 FastAPI serving model_en.vtt
007 CLIP model_en.vtt
006 Streamlit functions_en.vtt
003 FastAPI intro_en.vtt
002 Saving Models_en.vtt
005 Streamlit Intro_en.vtt
001 Intro_en.vtt
004 FastAPI serving model.mp4
006 Streamlit functions.mp4
007 CLIP model.mp4
003 FastAPI intro.mp4
002 Saving Models.mp4
005 Streamlit Intro.mp4
001 Intro.mp4
16 - Final Thoughts
001 Some advice on your journey_en.vtt
001 Some advice on your journey.mp4
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