Interpretability and Explainability in Deep Learning

Machine Learning › AI Deep Learning Theory Research

Interpretability and Explainability in Deep Learning

Interns will investigate techniques for understanding and visualizing what deep neural networks learn, including attention mechanisms, saliency maps, and feature visualization methods. They will work on developing tools to explain model predictions and identify potential biases in learned representations.

Choose your preferences above

Select Type, Mode and Duration to view available internship titles and fees.