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What are the challenges of making deep learning models explainable?
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Answer / Rajkumar Paswan
The challenges of making deep learning models explainable include their complexity, non-linearity, and lack of interpretability. These models often have many layers and parameters, making it difficult to understand how they arrive at a particular decision. Techniques like LIME and SHAP can help address these challenges.
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