Machine learning engineer

A thief is robbing a store and can carry a maximal weight of W into his knapsack. There are N items and the ith item weighs wi and is of value vi. Considering the constraints of the maximum weight that a knapsack can carry, you have to find and return the maximum value that a thief can generate by stealing items.

Asked so many questions, started from projects and gradually going deeper with technical concepts.
1. Asked about Vanishing Gradient.
2. Why use activation and why we use relu, how it works during back propagation.
3. Asked about residual Network and its concepts and how it helps for reducing back propagation with networks structure and everything.
4. Asked about the difference between LRP and LIME for explainable AI, why we use one over other or benefits of using LIME or LRP.
5. Asked about the process and algorithmic depths of LRP and LIME, what happens in the process, if you answer segmentation happens in LIME then he asked which segmentation and why, what happens if we take other segmentation method, question like this.
6. Asked about YOLOV3, how it is different from other and asked to explain entire YOLOV3 algorithmically.
7. Asked about Inception V3 and how it is different and helpful.
8. Asked about U-Net and how it is different. what is the use of parallel connection in Unet algorithmically.