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Statistical Modeling and Machine Learning in Heliophysics
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Statistical Modeling and Machine Learning in Heliophysics
General ML
Software Toolkit
Data Splits and Leakage
Model Evaluation
Neural Networks
Dense Neural Network for MNIST: PyTorch
Dense Neural Network for MNIST: Keras 3
Convolutional Neural Networks
Convolutional Network for MNIST: PyTorch
Convolutional Network for MNIST: Keras 3
CIFAR-10 CNN Progression
CIFAR-10 CNN Progression: PyTorch
CIFAR-10 CNN Progression: Keras 3
Tree Models and Ensembles
MNIST with XGBoost
Transfer Learning
CIFAR-10 Transfer Learning: PyTorch
CIFAR-10 Transfer Learning: Keras 3
Hyperparameter Tuning
Hyperparameter Tuning with Optuna: PyTorch
Hyperparameter Tuning with KerasTuner: Keras 3
Generative Models
Generative Adversarial Network for MNIST: PyTorch
Generative Adversarial Network for MNIST: Keras 3
Statistical Modeling
Heliophysics
Dst Forecasting
Dst Forecasting: PyTorch
Dst Forecasting: Keras 3
Plasma-Sheet Modeling
SEP Occurrence Forecasting
SEP Occurrence Forecasting: PyTorch
SEP Occurrence Forecasting: Keras 3
SEP Occurrence Forecasting with XGBoost
SEP Occurrence Forecasting: XGBoost
SEP Occurrence Forecasting: Repeated Validation
SEP Occurrence Forecasting: SHAP Attributions
Coronal-Loop Reconstruction
Coronal-Loop Reconstruction: PyTorch
Coronal-Loop Reconstruction: Keras 3
Resources
Index