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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
Neural Networks
Dense MNIST with PyTorch
Dense MNIST with Keras 3 — PyTorch Backend
Convolutional Neural Networks
MNIST Convolutional Neural Network with PyTorch
MNIST Convolutional Neural Network with Keras 3
CIFAR-10 CNN Progression
CIFAR-10 CNN Progression with Native PyTorch
CIFAR-10 CNN Progression with Keras 3 — PyTorch Backend
Tree Models and Ensembles
MNIST with XGBoost
Transfer Learning
CIFAR-10 Transfer Learning with PyTorch
CIFAR-10 Transfer Learning with Keras 3
Hyperparameter Tuning
MNIST CNN Tuning with Optuna and PyTorch
MNIST CNN Tuning with KerasTuner
Generative Models
MNIST DCGAN with Native PyTorch
MNIST DCGAN with Keras 3 — PyTorch Backend
Statistical Modeling
Heliophysics
Dst Forecasting
Dst Forecasting with Native PyTorch
Dst Forecasting with Keras 3 — PyTorch Backend
Plasma-Sheet Modeling
SEP Occurrence Forecasting
SEP Occurrence Forecasting — Native PyTorch
SEP Occurrence Forecasting — Keras 3 — PyTorch Backend
SEP Occurrence Forecasting — XGBoost and SHAP
SEP Occurrence Forecasting — XGBoost Demonstration
SEP Occurrence Forecasting — Repeated Sample-Level Validation
SEP Occurrence Forecasting — SHAP Demonstration
Coronal-Loop Reconstruction
Coronal-Loop Reconstruction — Native PyTorch
Coronal-Loop Reconstruction — Keras 3 — PyTorch Backend
Resources
Index