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| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
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| [Tutorialsplanet.NET].url |
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| [Tutorialsplanet.NET].url |
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| [Tutorialsplanet.NET].url |
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| 001 Intro_en.vtt |
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| 001 Intro_en.vtt |
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| 001 Intro.mp4 |
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| 001 Introduction_en.vtt |
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| 001 Introduction_en.vtt |
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| 001 Introduction.mp4 |
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| 001 Introduction and Terminology_en.vtt |
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| 001 Introduction to Transformers_en.vtt |
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| 001 Introduction to Transformers.mp4 |
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| 001 Kaggle part 1_en.vtt |
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| 001 Kaggle part 1.mp4 |
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| 001 Principal Component Analysis (PCA) theory_en.vtt |
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| 001 Principal Component Analysis (PCA) theory.mp4 |
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| 001 Some advice on your journey_en.vtt |
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| 001 Some advice on your journey.mp4 |
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| 001 Transfer Learning Introduction_en.vtt |
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| 001 Transfer Learning Introduction.mp4 |
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| 001 Word2vec and Embeddings_en.vtt |
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| 001 Word2vec and Embeddings.mp4 |
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| 001 Your reviews are important to me!.mp4 |
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| 002 Basic Data Structures_en.vtt |
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| 002 Basic Data Structures.mp4 |
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| 002 Bayesian Learning Distributions_en.vtt |
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| 002 Bayesian Learning Distributions.mp4 |
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| 002 Coco Dataset + Augmentations for Segmentation with Torchvision_en.vtt |
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| 002 Coco Dataset + Augmentations for Segmentation with Torchvision.mp4 |
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| 002 DL theory part 1_en.vtt |
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| 002 Fashion MNIST feed forward net for benchmarking_en.vtt |
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| 002 Fashion MNIST feed forward net for benchmarking.mp4 |
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| 002 Fashion MNIST PCA_en.vtt |
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| 002 Fashion MNIST PCA.mp4 |
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| 002 How to tackle this course_en.vtt |
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| 002 Kaggle + Word2Vec_en.vtt |
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| 002 ----------- Numpy -------------.html |
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| 002 Pytorch TensorDataset.mp4 |
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| 002 Saving Models_en.vtt |
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| 002 Stop words and Term Frequency_en.vtt |
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| 002 The illustrated Transformer (blogpost by Jay Alammar)_en.vtt |
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| 002 The illustrated Transformer (blogpost by Jay Alammar).mp4 |
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| 003 Bayes rule for population mean estimation.mp4 |
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| 003 Dictionaries_en.vtt |
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| 003 DL theory part 2_en.vtt |
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| 003 Encoder Transformer Models The Maths_en.vtt |
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| 003 FastAPI intro_en.vtt |
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| 005 Course Material.html |
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| 005 DBSCAN theory_en.vtt |
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| 005 Deep Learning - Long Short Term Memory (LSTM) Nets_en.vtt |
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| 005 Deep Learning - Long Short Term Memory (LSTM) Nets.mp4 |
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| 005 Dropout theory and code_en.vtt |
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| 005 Kaggle Multi-lingual Toxic Comment Classification Challenge_en.vtt |
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| 005 Kaggle Multi-lingual Toxic Comment Classification Challenge.mp4 |
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| 005 Kmeans part 2_en.vtt |
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| 005 Streamlit Intro_en.vtt |
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| 006 Broadcasting_en.vtt |
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| 006 Data Setup for Bayesian Linear Regression_en.vtt |
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| 006 Deep Learning - Stacking LSTMs + GRUs_en.vtt |
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| 006 Deep Learning with Pytorch Stochastic Gradient Descent_en.vtt |
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| 006 Deep Learning with Pytorch Stochastic Gradient Descent.mp4 |
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| 006 First example with Relu_en.vtt |
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| 006 Gaussian Mixture Models (GMM) theory_en.vtt |
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| 006 MaxPool (and comparison to stride)_en.vtt |
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| 006 PyTorch Hooks Step through with breakpoints_en.vtt |
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| 006 PyTorch Lightning Trainer + Model evaluation_en.vtt |
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| 006 Sklearn classification prelude_en.vtt |
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| 006 Tokenizers and data prep for BERT models_en.vtt |
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| 006 Tokenizers and data prep for BERT models.mp4 |
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| 007 Bayesian Linear Regression with pymc3_en.vtt |
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| 007 Bayesian Linear Regression with pymc3.mp4 |
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| 007 Cifar-10_en.vtt |
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| 007 CLIP model_en.vtt |
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| 007 Deep Learning for Cassava Leaf Classification_en.vtt |
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| 007 Deep Learning for Cassava Leaf Classification.mp4 |
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| 007 Deep Learning with Pytorch Optimizers_en.vtt |
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| 007 Distilbert (Smaller BERT) model_en.vtt |
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| 007 MNIST and Softmax_en.vtt |
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| 007 PyTorch Weighted CrossEntropy Loss_en.vtt |
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| 007 ---------------- Scikit Learn -------------------------------------.html |
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| 007 Transfer Learning - GLOVE vectors_en.vtt |
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| 007 Word (feature) importance_en.vtt |
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| 008 Bayesian Rolling Regression - Problem setup_en.vtt |
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| 008 Bayesian Rolling Regression - Problem setup.mp4 |
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| 008 Cassava Leaf Dataset_en.vtt |
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| 008 Dealing with missing values_en.vtt |
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| 008 Deep Learning Input Normalisation_en.vtt |
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| 008 Dictionaries again_en.vtt |
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| 008 Intro_en.vtt |
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| 008 Nose Tip detection with CNNs_en.vtt |
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| 008 Nose Tip detection with CNNs.mp4 |
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| 008 Pytorch Lightning + DistilBERT for classification_en.vtt |
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| 008 Pytorch Lightning + DistilBERT for classification.mp4 |
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| 008 Pytorch Model API_en.vtt |
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| 008 Sequence to Sequence Introduction + Data Prep_en.vtt |
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| 008 Spacy intro_en.vtt |
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| 008 Weights and Biases Logging images_en.vtt |
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| 009 Bayesian Rolling regression - pymc3 way_en.vtt |
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| 009 Bayesian Rolling regression - pymc3 way.mp4 |
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| 009 Data Augmentation with Torchvision Transforms_en.vtt |
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| 009 Data Augmentation with Torchvision Transforms.mp4 |
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| 009 Feature Extraction with Spacy (using Pandas)_en.vtt |
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| 009 Feature Extraction with Spacy (using Pandas).mp4 |
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| 009 Linear Regresson Part 1_en.vtt |
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| 009 -------------------------------- Pandas --------------------------------.html |
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| 009 Pytorch in GPUs_en.vtt |
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| 009 Semantic Segmentation training with PyTorch Lightning_en.vtt |
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| 009 Semantic Segmentation training with PyTorch Lightning.mp4 |
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| 009 Sequence to Sequence model + Keras Model API_en.vtt |
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| 009 --------- Time Series -------------------.html |
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| 010 Batch Norm_en.vtt |
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| 010 Batch Norm.mp4 |
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| 010 Bayesian Rolling Regression - forecasting_en.vtt |
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| 010 Classification Example_en.vtt |
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| 010 Deep Learning Intro to Pytorch Lightning_en.vtt |
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| 010 Deep Learning Intro to Pytorch Lightning.mp4 |
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| 010 Intro_en.vtt |
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| 010 Train vs Test Augmentations + DataLoader parameters.mp4 |
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| 011 Batch Norm Theory_en.vtt |
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| 011 Classification and Regression Trees_en.vtt |
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| 011 Deep Learning Transfer Learning Model with ResNet_en.vtt |
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| 011 Deep Learning Transfer Learning Model with ResNet.mp4 |
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| 011 Loss functions_en.vtt |
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| 011 Over-sampling_en.vtt |
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| 011 Pandas simple functions_en.vtt |
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| 011 Variational Bayes Intro_en.vtt |
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| 012 CART part 2_en.vtt |
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| 012 CART part 2.mp4 |
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| 012 FB Prophet part 1_en.vtt |
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| 012 Pandas Subsetting.mp4 |
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| 012 -------- Regularization ------------.html |
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| 012 Setting up PyTorch Lightning for training.mp4 |
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| 012 Variational Bayes Linear Classification.mp4 |
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| 013 Cross Entropy Loss for Imbalanced Classes_en.vtt |
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| 013 Cross Entropy Loss for Imbalanced Classes.mp4 |
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| 013 Random Forest theory_en.vtt |
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| 013 Variational Bayesian Inference Result Analysis_en.vtt |
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| 013 Variational Bayesian Inference Result Analysis.mp4 |
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| 014 Minibatch Variational Bayes_en.vtt |
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| 014 Random Forest Code.mp4 |
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| 014 Theory behind FB Prophet_en.vtt |
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| 015 Deep Bayesian Networks_en.vtt |
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| 015 L2 Loss Ridge Regression intro_en.vtt |
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| 015 ------------ Model Diagnostics -----.html |
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| 015 WandB for logging experiments_en.vtt |
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| 016 Deep Bayesian Networks - analysis_en.vtt |
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| 016 Overfitting_en.vtt |
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| 017 Cross Validation_en.vtt |
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| 017 ----- Plotting --------.html |
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| 017 S&P500 data preparation for L1 loss.mp4 |
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| 018 L1 Penalised Regression (Lasso)_en.vtt |
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| 019 Area Under Curve (AUC) Part 1_en.vtt |
9.21Кб |
| 019 Area Under Curve (AUC) Part 1.mp4 |
84.11Мб |
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3.78Кб |
| 019 L1 L2 Penalty theory why it works.mp4 |
23.22Мб |
| 019 Line plot_en.vtt |
3.24Кб |
| 019 Line plot.mp4 |
8.55Мб |
| 020 Area Under Curve (AUC) Part 2_en.vtt |
6.96Кб |
| 020 Area Under Curve (AUC) Part 2.mp4 |
19.50Мб |
| 020 Plot multiple lines_en.vtt |
3.91Кб |
| 020 Plot multiple lines.mp4 |
45.39Мб |
| 021 Histograms_en.vtt |
7.87Кб |
| 021 Histograms.mp4 |
21.62Мб |
| 022 Scatter Plots_en.vtt |
6.39Кб |
| 022 Scatter Plots.mp4 |
18.60Мб |
| 023 Subplots_en.vtt |
6.00Кб |
| 023 Subplots.mp4 |
15.31Мб |
| 024 Seaborn + pair plots_en.vtt |
7.95Кб |
| 024 Seaborn + pair plots.mp4 |
49.67Мб |
| 30889860-course-code-material.zip |
26.20Мб |
| 31237618-03-0-plotting.zip |
2.80Мб |
| 31283222-multi-plot.py |
440б |
| 31762302-06-0-reguralisation.zip |
2.56Мб |
| 31919076-bayesian-inference.zip |
1.80Мб |
| 32725408-09-tensorflow.zip |
2.66Мб |
| 34142844-04-pairplots.ipynb |
200.49Кб |
| external-assets-links.txt |
122б |
| external-assets-links.txt |
52б |
| external-assets-links.txt |
264б |