AI and computer vision internship interviews usually cover four things: ML fundamentals (overfitting, train/validation/test splits, evaluation metrics), computer vision basics (convolutions, object detection vs classification, IoU), Python skills, and a deep dive into one of your projects. Being able to explain your own project clearly matters most.
What is overfitting and how do you reduce it? Why do we need a validation set? When would you use precision/recall instead of accuracy? What does a learning rate do?
What does a convolution layer do? What's the difference between classification, detection and segmentation? What is IoU, and what is non-maximum suppression used for? How would you handle a class with very few images?
Expect: what was the problem, why did you choose that model, what went wrong, and what would you do differently. Honest, specific answers about failures land better than polished claims.
Anyone preparing for an AI or computer vision internship interview, including for the opening linked here.
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