Contents:
0.1. installation
0.2. eda
1. solving regression problem with ML
2. solving classification problem with ML
3. neural networks for regression
4. neural network for classification
5. activations
6. building neural network with PyTorch
7. training pytorch based models
8. loading model from config
9. model evaluation
10. input data formats
1. hyperparameter optimization using Model class
2. hyperparameter optimizing using HyperOpt class
1. compare ml models for regression
2. compare ml models for classification
3. compare dl models
4. compare transformations
1. data preparation
2. handling categorical data
3. handling missing values
4. data transformation
5. dimensionality reduction
6. feature creation
7. HRU discretization
1. postprocessing of regression results
2. postprocessing of classification results
3. visualizing decision trees
4. visualizing neural networks
5. interpretable machine learning
6. interpretable deep learning
7. model agnostic interpretaion methods
1. ai4water datasts
2. working with large data
3. multi-output neural network
4. multi-input neural network
5. using conditional RNN
6. Quantile regression
7. Autoencoders and variational autoencoders
8. using third party models
9. customizing training loop in tensorflow
10. customizing training loop in pytorch
11. customizing loss function in tensorflow
12. customizing loss function in pytorch
1. cloud cover detection
2. crop disease detection
3. flood prediction
4. map floodwater
5. seismic facies
6. snow cast
7. space net
8. spot the crop
9. weather forecasting
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