Geometric_Layers.md

graph LR
    Geometric_Layers["Geometric Layers"]
    Core_Green_Models["Core Green Models"]
    Data_Utilities["Data Utilities"]
    Wavelet_Layers["Wavelet Layers"]
    Research_Utilities["Research Utilities"]
    Core_Green_Models -- "utilizes" --> Geometric_Layers
    Core_Green_Models -- "utilizes" --> Data_Utilities
    Core_Green_Models -- "utilizes" --> Wavelet_Layers
    Research_Utilities -- "interacts with" --> Core_Green_Models
    Research_Utilities -- "interacts with" --> Data_Utilities
    click Geometric_Layers href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/neuro-green/Geometric_Layers.md" "Details"
    click Wavelet_Layers href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/neuro-green/Wavelet_Layers.md" "Details"

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Details

Deep Learning Research Library/Framework

Geometric Layers [Expand]

This component implements specialized neural network layers and operations designed to process Symmetric Positive Definite (SPD) matrices. It leverages geometric deep learning principles to handle covariance matrices or other SPD-valued data directly within the neural network architecture, enabling the model to operate on the Riemannian manifold of SPD matrices.

Related Classes/Methods:

• green.spd_layers (1:100)

Core Green Models

This component encompasses the primary deep learning models that utilize the Geometric Layers for processing Symmetric Positive Definite (SPD) data. These models are likely built using PyTorch Lightning for structured training and experimentation.

Related Classes/Methods:

• green.research_code.pl_utils (1:100)

Data Utilities

This component provides utility functions and classes for handling and preparing data, likely including transformations, loading, and preprocessing steps necessary for the Core Green Models to consume.

Related Classes/Methods:

• green.data_utils (1:100)

Wavelet Layers [Expand]

This component likely implements neural network layers or operations that incorporate wavelet transforms, potentially for feature extraction or signal processing within the deep learning models.

Related Classes/Methods:

• green.wavelet_layers (1:100)

Research Utilities

This component contains various utility functions and scripts specifically designed to support research activities, such as cross-validation, example notebooks, and general PyTorch Lightning utilities for experimental setups.

Related Classes/Methods:

• green.research_code.crossval_utils (1:100)
• green.research_code.example (1:100)
• green.research_code.example_wo_wav (1:100)
• green.research_code.pl_utils (1:100)

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