Machine Learning Deep Learning and Bayesian Learning | C517199ADB7A8B3A2C9B90B6726F0B415EC0E2FB

Machine Learning Deep Learning and Bayesian Learning

Name	Machine Learning, Deep Learning and Bayesian Learning	DOWNLOAD Copy Link
Total Size	6.0 GB
Total Files	548
Last Seen	2025-02-19 23:39
Hash	C517199ADB7A8B3A2C9B90B6726F0B415EC0E2FB

/.../03 - Machine Learning Numpy + Scikit Learn/
012 CART part 2.mp4	174.6 MB
012 CART part 2_en.vtt	21.0 KB
005 Kmeans part 2_en.vtt	20.2 KB
003 Gradient Descent_en.vtt	17.0 KB
009 Linear Regresson Part 1_en.vtt	12.5 KB
004 Kmeans part 1_en.vtt	12.1 KB
010 Linear Regression Part 2_en.vtt	11.5 KB
015 Gradient Boosted Machines_en.vtt	9.9 KB
006 Broadcasting_en.vtt	9.9 KB
002 ----------- Numpy -------------.html	0.1 KB
007 ---------------- Scikit Learn -------------------------------------.html	0.1 KB
013 Random Forest theory_en.vtt	2.6 KB
014 Random Forest Code_en.vtt	6.8 KB
011 Classification and Regression Trees_en.vtt	6.6 KB
008 Intro_en.vtt	5.1 KB
009 Linear Regresson Part 1.mp4	94.9 MB
004 Kmeans part 1.mp4	82.2 MB
010 Linear Regression Part 2.mp4	75.0 MB
015 Gradient Boosted Machines.mp4	70.9 MB
005 Kmeans part 2.mp4	66.3 MB
003 Gradient Descent.mp4	45.5 MB
014 Random Forest Code.mp4	38.5 MB
008 Intro.mp4	37.1 MB
006 Broadcasting.mp4	28.5 MB
011 Classification and Regression Trees.mp4	21.0 MB
013 Random Forest theory.mp4	5.1 MB
001 Your reviews are important to me!.mp4	2.1 MB
/
TutsNode.com.txt	0.1 KB
[TGx]Downloaded from torrentgalaxy.to .txt	0.6 KB
/.../02 - Basic python + Pandas + Plotting/
34142844-04-pairplots.ipynb	205.3 KB
001 Intro_en.vtt	0.9 KB
011 Pandas simple functions_en.vtt	11.7 KB
005 Numpy functions_en.vtt	10.9 KB
009 -------------------------------- Pandas --------------------------------.html	0.1 KB
010 Intro_en.vtt	2.5 KB
017 ----- Plotting --------.html	0.0 KB
018 Plotting resources (notebooks).html	0.1 KB
31283222-multi-plot.py	0.4 KB
015 Pandas map and apply_en.vtt	8.4 KB
024 Seaborn + pair plots_en.vtt	8.1 KB
021 Histograms_en.vtt	8.1 KB
013 Pandas loc and iloc_en.vtt	7.8 KB
016 Pandas groupby_en.vtt	7.2 KB
002 Basic Data Structures_en.vtt	6.6 KB
022 Scatter Plots_en.vtt	6.5 KB
012 Pandas Subsetting_en.vtt	6.4 KB
023 Subplots_en.vtt	6.1 KB
004 Python functions (methods)_en.vtt	5.7 KB
014 Pandas loc and iloc 2_en.vtt	5.3 KB
007 For loops_en.vtt	4.3 KB
006 Conditional statements_en.vtt	4.0 KB
020 Plot multiple lines_en.vtt	4.0 KB
003 Dictionaries_en.vtt	3.9 KB
019 Line plot_en.vtt	3.3 KB
008 Dictionaries again_en.vtt	3.2 KB
005 Numpy functions.mp4	65.5 MB
024 Seaborn + pair plots.mp4	52.1 MB
020 Plot multiple lines.mp4	47.6 MB
013 Pandas loc and iloc.mp4	43.8 MB
011 Pandas simple functions.mp4	40.2 MB
015 Pandas map and apply.mp4	33.0 MB
004 Python functions (methods).mp4	28.9 MB
012 Pandas Subsetting.mp4	23.1 MB
002 Basic Data Structures.mp4	23.0 MB
021 Histograms.mp4	22.7 MB
003 Dictionaries.mp4	19.7 MB
022 Scatter Plots.mp4	19.5 MB
016 Pandas groupby.mp4	19.2 MB
023 Subplots.mp4	16.1 MB
014 Pandas loc and iloc 2.mp4	14.5 MB
006 Conditional statements.mp4	13.2 MB
007 For loops.mp4	13.0 MB
019 Line plot.mp4	9.0 MB
008 Dictionaries again.mp4	6.5 MB
010 Intro.mp4	5.3 MB
001 Intro.mp4	3.0 MB
31237618-03-0-plotting.zip	2.9 MB
/01 - Introduction/
001 Introduction_en.vtt	2.3 KB
005 Course Material.html	0.1 KB
002 How to tackle this course_en.vtt	6.4 KB
004 Jupyter Notebooks_en.vtt	5.1 KB
003 Installations and sign ups_en.vtt	4.9 KB
002 How to tackle this course.mp4	51.2 MB
003 Installations and sign ups.mp4	44.9 MB
001 Introduction.mp4	43.8 MB
30889860-course-code-material.zip	27.5 MB
004 Jupyter Notebooks.mp4	9.1 MB
/.../13 - Deep Learning Transformers and BERT/
008 Pytorch Lightning + DistilBERT for classification_en.vtt	17.7 KB
006 Tokenizers and data prep for BERT models_en.vtt	11.0 KB
007 Distilbert (Smaller BERT) model_en.vtt	11.0 KB
002 The illustrated Transformer (blogpost by Jay Alammar)_en.vtt	9.2 KB
003 Encoder Transformer Models The Maths_en.vtt	5.7 KB
004 BERT - The theory_en.vtt	3.9 KB
005 Kaggle Multi-lingual Toxic Comment Classification Challenge_en.vtt	2.0 KB
001 Introduction to Transformers_en.vtt	1.7 KB
external-assets-links.txt	0.3 KB
008 Pytorch Lightning + DistilBERT for classification.mp4	107.7 MB
007 Distilbert (Smaller BERT) model.mp4	51.2 MB
006 Tokenizers and data prep for BERT models.mp4	30.5 MB
003 Encoder Transformer Models The Maths.mp4	30.1 MB
002 The illustrated Transformer (blogpost by Jay Alammar).mp4	24.7 MB
004 BERT - The theory.mp4	8.5 MB
005 Kaggle Multi-lingual Toxic Comment Classification Challenge.mp4	7.1 MB
001 Introduction to Transformers.mp4	3.6 MB
/.../07 - Deep Learning/
004 Tensorflow + Keras demo problem 1_en.vtt	16.8 KB
001 Intro.mp4	647.8 KB
007 MNIST and Softmax_en.vtt	10.7 KB
011 Batch Norm Theory_en.vtt	8.5 KB
002 DL theory part 1_en.vtt	6.3 KB
010 Batch Norm_en.vtt	5.8 KB
009 Softmax theory_en.vtt	5.7 KB
005 Activation functions_en.vtt	5.6 KB
006 First example with Relu_en.vtt	5.5 KB
003 DL theory part 2_en.vtt	4.0 KB
008 Deep Learning Input Normalisation_en.vtt	3.2 KB
001 Intro_en.vtt	0.5 KB
009 Softmax theory.mp4	61.2 MB
007 MNIST and Softmax.mp4	58.5 MB
011 Batch Norm Theory.mp4	56.5 MB
004 Tensorflow + Keras demo problem 1.mp4	45.4 MB
006 First example with Relu.mp4	34.2 MB
003 DL theory part 2.mp4	23.9 MB
002 DL theory part 1.mp4	18.1 MB
010 Batch Norm.mp4	17.9 MB
005 Activation functions.mp4	16.1 MB
008 Deep Learning Input Normalisation.mp4	10.9 MB
32725408-09-tensorflow.zip	2.8 MB
/.../12 - Pixel Level Segmentation (Semantic Segmentation) with PyTorch/
009 Semantic Segmentation training with PyTorch Lightning_en.vtt	16.6 KB
009 Semantic Segmentation training with PyTorch Lightning.mp4	136.5 MB
007 PyTorch Weighted CrossEntropy Loss_en.vtt	9.3 KB
006 PyTorch Hooks Step through with breakpoints_en.vtt	9.0 KB
005 PyTorch Hooks_en.vtt	7.5 KB
003 Unet Architecture overview_en.vtt	6.5 KB
002 Coco Dataset + Augmentations for Segmentation with Torchvision_en.vtt	6.1 KB
004 PyTorch Model Architecture_en.vtt	3.7 KB
001 Introduction_en.vtt	2.6 KB
008 Weights and Biases Logging images_en.vtt	2.0 KB
external-assets-links.txt	0.1 KB
006 PyTorch Hooks Step through with breakpoints.mp4	70.8 MB
007 PyTorch Weighted CrossEntropy Loss.mp4	68.4 MB
001 Introduction.mp4	26.5 MB
005 PyTorch Hooks.mp4	25.9 MB
002 Coco Dataset + Augmentations for Segmentation with Torchvision.mp4	19.8 MB
008 Weights and Biases Logging images.mp4	16.6 MB
003 Unet Architecture overview.mp4	15.4 MB
004 PyTorch Model Architecture.mp4	14.2 MB
/.../04 - Machine Learning Classification + Time Series + Model Diagnostics/
005 Titanic dataset_en.vtt	15.6 KB
007 Sklearn classification_en.vtt	14.8 KB
018 Stratified K Fold_en.vtt	10.2 KB
012 FB Prophet part 1_en.vtt	10.0 KB
019 Area Under Curve (AUC) Part 1_en.vtt	9.4 KB
017 Cross Validation_en.vtt	8.5 KB
011 Loss functions_en.vtt	7.3 KB
001 Kaggle part 1_en.vtt	2.7 KB
016 Overfitting_en.vtt	7.2 KB
020 Area Under Curve (AUC) Part 2_en.vtt	7.1 KB
003 Theory part 1_en.vtt	6.9 KB
004 Theory part 2 + code_en.vtt	6.4 KB
009 --------- Time Series -------------------.html	0.3 KB
010 Intro_en.vtt	6.1 KB
014 Theory behind FB Prophet_en.vtt	6.0 KB
008 Dealing with missing values_en.vtt	5.9 KB
015 ------------ Model Diagnostics -----.html	0.1 KB
006 Sklearn classification prelude_en.vtt	5.4 KB
013 FB Prophet part 2_en.vtt	4.2 KB
002 Kaggle part 2_en.vtt	3.3 KB
005 Titanic dataset.mp4	122.0 MB
007 Sklearn classification.mp4	94.4 MB
019 Area Under Curve (AUC) Part 1.mp4	88.2 MB
012 FB Prophet part 1.mp4	81.8 MB
018 Stratified K Fold.mp4	60.9 MB
017 Cross Validation.mp4	56.3 MB
008 Dealing with missing values.mp4	53.2 MB
011 Loss functions.mp4	48.7 MB
004 Theory part 2 + code.mp4	28.6 MB
013 FB Prophet part 2.mp4	25.6 MB
020 Area Under Curve (AUC) Part 2.mp4	20.4 MB
016 Overfitting.mp4	20.3 MB
014 Theory behind FB Prophet.mp4	17.7 MB
006 Sklearn classification prelude.mp4	15.0 MB
003 Theory part 1.mp4	14.2 MB
010 Intro.mp4	12.0 MB
002 Kaggle part 2.mp4	11.7 MB
001 Kaggle part 1.mp4	7.1 MB
/.../09 - Deep Learning Recurrent Neural Nets/
003 Word2Vec keras Model API_en.vtt	13.6 KB
010 Sequence to Sequence models Prediction step_en.vtt	13.5 KB
005 Deep Learning - Long Short Term Memory (LSTM) Nets_en.vtt	12.1 KB
007 Transfer Learning - GLOVE vectors_en.vtt	11.7 KB
004 Recurrent Neural Nets - Theory_en.vtt	10.8 KB
002 Kaggle + Word2Vec_en.vtt	10.8 KB
009 Sequence to Sequence model + Keras Model API_en.vtt	8.9 KB
001 Word2vec and Embeddings_en.vtt	8.5 KB
008 Sequence to Sequence Introduction + Data Prep_en.vtt	8.2 KB
006 Deep Learning - Stacking LSTMs + GRUs_en.vtt	2.2 KB
010 Sequence to Sequence models Prediction step.mp4	109.8 MB
005 Deep Learning - Long Short Term Memory (LSTM) Nets.mp4	95.4 MB
008 Sequence to Sequence Introduction + Data Prep.mp4	84.0 MB
007 Transfer Learning - GLOVE vectors.mp4	78.2 MB
003 Word2Vec keras Model API.mp4	47.4 MB
001 Word2vec and Embeddings.mp4	46.1 MB
009 Sequence to Sequence model + Keras Model API.mp4	32.0 MB
002 Kaggle + Word2Vec.mp4	29.1 MB
004 Recurrent Neural Nets - Theory.mp4	20.0 MB
006 Deep Learning - Stacking LSTMs + GRUs.mp4	5.3 MB
/.../08 - Deep Learning (TensorFlow) - Convolutional Neural Nets/
008 Nose Tip detection with CNNs_en.vtt	12.8 KB
007 Cifar-10_en.vtt	10.3 KB
003 Keras Conv2D layer_en.vtt	8.8 KB
005 Dropout theory and code_en.vtt	7.2 KB
006 MaxPool (and comparison to stride)_en.vtt	5.5 KB
002 Fashion MNIST feed forward net for benchmarking_en.vtt	3.6 KB
001 Intro_en.vtt	3.3 KB
004 Model fitting and discussion of results_en.vtt	3.0 KB
008 Nose Tip detection with CNNs.mp4	72.0 MB
003 Keras Conv2D layer.mp4	46.6 MB
007 Cifar-10.mp4	28.6 MB
005 Dropout theory and code.mp4	24.8 MB
002 Fashion MNIST feed forward net for benchmarking.mp4	20.6 MB
006 MaxPool (and comparison to stride).mp4	18.5 MB
004 Model fitting and discussion of results.mp4	18.3 MB
001 Intro.mp4	6.3 MB
/.../05 - Unsupervised Learning/
002 Fashion MNIST PCA_en.vtt	10.7 KB
001 Principal Component Analysis (PCA) theory_en.vtt	9.2 KB
006 Gaussian Mixture Models (GMM) theory_en.vtt	8.1 KB
003 K-means_en.vtt	7.8 KB
004 Other clustering methods_en.vtt	7.3 KB
005 DBSCAN theory_en.vtt	7.1 KB
002 Fashion MNIST PCA.mp4	107.1 MB
004 Other clustering methods.mp4	50.4 MB
003 K-means.mp4	23.4 MB
001 Principal Component Analysis (PCA) theory.mp4	21.5 MB
006 Gaussian Mixture Models (GMM) theory.mp4	21.0 MB
005 DBSCAN theory.mp4	13.9 MB
/.../11 - Deep Learning Transfer Learning with PyTorch Lightning/
010 Train vs Test Augmentations + DataLoader parameters_en.vtt	3.4 KB
006 PyTorch Lightning Trainer + Model evaluation_en.vtt	6.5 KB
009 Data Augmentation with Torchvision Transforms_en.vtt	6.0 KB
015 WandB for logging experiments_en.vtt	5.5 KB
008 Cassava Leaf Dataset_en.vtt	5.0 KB
004 PyTorch transfer learning with ResNet_en.vtt	4.5 KB
003 PyTorch datasets + Torchvision_en.vtt	4.3 KB
013 Cross Entropy Loss for Imbalanced Classes_en.vtt	4.0 KB
005 PyTorch Lightning Model_en.vtt	4.0 KB
012 Setting up PyTorch Lightning for training_en.vtt	3.6 KB
011 Deep Learning Transfer Learning Model with ResNet_en.vtt	3.4 KB
014 PyTorch Test dataset setup and evaluation_en.vtt	2.9 KB
002 Kaggle problem description_en.vtt	2.9 KB
001 Transfer Learning Introduction_en.vtt	2.0 KB
007 Deep Learning for Cassava Leaf Classification_en.vtt	1.1 KB
009 Data Augmentation with Torchvision Transforms.mp4	59.3 MB
006 PyTorch Lightning Trainer + Model evaluation.mp4	52.7 MB
015 WandB for logging experiments.mp4	22.6 MB
004 PyTorch transfer learning with ResNet.mp4	16.2 MB
008 Cassava Leaf Dataset.mp4	16.0 MB
003 PyTorch datasets + Torchvision.mp4	15.4 MB
005 PyTorch Lightning Model.mp4	9.9 MB
002 Kaggle problem description.mp4	9.6 MB
013 Cross Entropy Loss for Imbalanced Classes.mp4	8.9 MB
012 Setting up PyTorch Lightning for training.mp4	8.8 MB
011 Deep Learning Transfer Learning Model with ResNet.mp4	8.4 MB
010 Train vs Test Augmentations + DataLoader parameters.mp4	8.1 MB
014 PyTorch Test dataset setup and evaluation.mp4	7.4 MB
001 Transfer Learning Introduction.mp4	4.7 MB
007 Deep Learning for Cassava Leaf Classification.mp4	4.3 MB
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/.../14 - Bayesian Learning and probabilistic programming/
002 Bayesian Learning Distributions_en.vtt	10.7 KB
007 Bayesian Linear Regression with pymc3_en.vtt	10.2 KB
009 Bayesian Rolling regression - pymc3 way_en.vtt	9.5 KB
003 Bayes rule for population mean estimation_en.vtt	9.2 KB
004 Bayesian learning Population estimation pymc3 way_en.vtt	9.1 KB
001 Introduction and Terminology_en.vtt	8.5 KB
005 Coin Toss Example with Pymc3_en.vtt	8.2 KB
012 Variational Bayes Linear Classification_en.vtt	7.7 KB
008 Bayesian Rolling Regression - Problem setup_en.vtt	5.7 KB
010 Bayesian Rolling Regression - forecasting_en.vtt	5.5 KB
006 Data Setup for Bayesian Linear Regression_en.vtt	4.8 KB
016 Deep Bayesian Networks - analysis_en.vtt	4.2 KB
014 Minibatch Variational Bayes_en.vtt	4.0 KB
013 Variational Bayesian Inference Result Analysis_en.vtt	3.8 KB
011 Variational Bayes Intro_en.vtt	3.3 KB
015 Deep Bayesian Networks_en.vtt	3.2 KB
005 Coin Toss Example with Pymc3.mp4	74.1 MB
004 Bayesian learning Population estimation pymc3 way.mp4	74.0 MB
007 Bayesian Linear Regression with pymc3.mp4	63.0 MB
009 Bayesian Rolling regression - pymc3 way.mp4	57.4 MB
003 Bayes rule for population mean estimation.mp4	52.6 MB
012 Variational Bayes Linear Classification.mp4	46.4 MB
002 Bayesian Learning Distributions.mp4	37.7 MB
010 Bayesian Rolling Regression - forecasting.mp4	31.8 MB
001 Introduction and Terminology.mp4	19.0 MB
006 Data Setup for Bayesian Linear Regression.mp4	17.9 MB
008 Bayesian Rolling Regression - Problem setup.mp4	15.6 MB
014 Minibatch Variational Bayes.mp4	11.6 MB
016 Deep Bayesian Networks - analysis.mp4	11.0 MB
011 Variational Bayes Intro.mp4	9.1 MB
013 Variational Bayesian Inference Result Analysis.mp4	7.7 MB
015 Deep Bayesian Networks.mp4	7.6 MB
31919076-bayesian-inference.zip	1.9 MB
/.../06 - Natural Language Processing + Regularization/
004 Financial News Sentiment Classifier_en.vtt	10.2 KB
009 Feature Extraction with Spacy (using Pandas)_en.vtt	10.1 KB
016 Ridge regression (L2 penalised regression)_en.vtt	8.1 KB
005 NLTK + Stemming_en.vtt	8.0 KB
017 S&P500 data preparation for L1 loss_en.vtt	7.3 KB
014 MSE recap_en.vtt	6.3 KB
011 Over-sampling_en.vtt	5.9 KB
018 L1 Penalised Regression (Lasso)_en.vtt	5.7 KB
008 Spacy intro_en.vtt	5.7 KB
001 Intro_en.vtt	5.5 KB
002 Stop words and Term Frequency_en.vtt	5.1 KB
010 Classification Example_en.vtt	4.4 KB
006 N-grams_en.vtt	4.1 KB
019 L1 L2 Penalty theory why it works_en.vtt	3.9 KB
007 Word (feature) importance_en.vtt	3.8 KB
012 -------- Regularization ------------.html	0.2 KB
013 Introduction_en.vtt	2.7 KB
015 L2 Loss Ridge Regression intro_en.vtt	3.7 KB
003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory_en.vtt	3.1 KB
009 Feature Extraction with Spacy (using Pandas).mp4	80.2 MB
016 Ridge regression (L2 penalised regression).mp4	49.3 MB
005 NLTK + Stemming.mp4	47.8 MB
004 Financial News Sentiment Classifier.mp4	35.3 MB
008 Spacy intro.mp4	34.8 MB
011 Over-sampling.mp4	34.4 MB
018 L1 Penalised Regression (Lasso).mp4	32.9 MB
017 S&P500 data preparation for L1 loss.mp4	26.4 MB
010 Classification Example.mp4	25.3 MB
019 L1 L2 Penalty theory why it works.mp4	24.3 MB
014 MSE recap.mp4	19.2 MB
006 N-grams.mp4	14.5 MB
007 Word (feature) importance.mp4	13.0 MB
002 Stop words and Term Frequency.mp4	11.2 MB
001 Intro.mp4	10.9 MB
015 L2 Loss Ridge Regression intro.mp4	10.5 MB
013 Introduction.mp4	8.8 MB
003 Term Frequency - Inverse Document Frequency (Tf - Idf) theory.mp4	6.3 MB
31762302-06-0-reguralisation.zip	2.7 MB
/.../10 - Deep Learning PyTorch Introduction/
010 Deep Learning Intro to Pytorch Lightning_en.vtt	9.5 KB
005 Deep Learning with Pytorch Loss functions_en.vtt	8.9 KB
006 Deep Learning with Pytorch Stochastic Gradient Descent_en.vtt	8.3 KB
003 Pytorch Dataset and DataLoaders_en.vtt	5.9 KB
004 Deep Learning with PyTorch nn.Sequential models_en.vtt	5.8 KB
008 Pytorch Model API_en.vtt	5.6 KB
002 Pytorch TensorDataset_en.vtt	5.1 KB
007 Deep Learning with Pytorch Optimizers_en.vtt	3.5 KB
009 Pytorch in GPUs_en.vtt	2.6 KB
001 Introduction_en.vtt	1.3 KB
external-assets-links.txt	0.1 KB
006 Deep Learning with Pytorch Stochastic Gradient Descent.mp4	83.3 MB
005 Deep Learning with Pytorch Loss functions.mp4	55.0 MB
010 Deep Learning Intro to Pytorch Lightning.mp4	54.9 MB
003 Pytorch Dataset and DataLoaders.mp4	37.1 MB
008 Pytorch Model API.mp4	34.9 MB
002 Pytorch TensorDataset.mp4	13.0 MB
004 Deep Learning with PyTorch nn.Sequential models.mp4	11.6 MB
007 Deep Learning with Pytorch Optimizers.mp4	10.7 MB
009 Pytorch in GPUs.mp4	5.2 MB
001 Introduction.mp4	2.3 MB
/.../15 - Model Deployment/
004 FastAPI serving model_en.vtt	7.7 KB
007 CLIP model_en.vtt	7.5 KB
006 Streamlit functions_en.vtt	6.2 KB
003 FastAPI intro_en.vtt	5.4 KB
002 Saving Models_en.vtt	3.2 KB
005 Streamlit Intro_en.vtt	2.6 KB
001 Intro_en.vtt	1.2 KB
004 FastAPI serving model.mp4	30.7 MB
006 Streamlit functions.mp4	21.8 MB
007 CLIP model.mp4	19.7 MB
003 FastAPI intro.mp4	12.2 MB
002 Saving Models.mp4	7.9 MB
005 Streamlit Intro.mp4	6.2 MB
001 Intro.mp4	2.6 MB
/.../16 - Final Thoughts/
001 Some advice on your journey_en.vtt	3.9 KB
001 Some advice on your journey.mp4	14.2 MB
Total files 548