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Grad-CAM Analysis
Advanced gradient-based visualization for deep learning model interpretability
Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique that produces a coarse localization map highlighting the important regions in the image for predicting the concept.
Grad-CAM Analysis
Advanced gradient-based visualization for deep learning model interpretability
SHAP Analysis
SHapley Additive exPlanations for model feature importance analysis
SHAP (SHapley Additive exPlanations) values provide a unified approach to explain the output of any machine learning model by measuring the contribution of each feature.
SHAP Analysis
SHapley Additive exPlanations for model feature importance analysis
Training Curves
Comprehensive training and validation metrics visualization
Training curves show the progression of model performance over epochs, helping identify overfitting, underfitting, and optimal training duration.
Training Curves
Comprehensive training and validation metrics visualization
LIME Analysis
Local Interpretable Model-agnostic Explanations for model transparency
LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the model locally around the prediction.
LIME Analysis
Local Interpretable Model-agnostic Explanations for model transparency
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