Annexure 10: PyTorch Problems for University Exams
Abstract: Below is the Annexure 10: PyTorch Problems for University Exams — well-structured, university-level, and divided into Short Answer , Long Answer , Coding , and Case Study problems. This annexure is ready to be inserted into the book. **Annexure 10 PyTorch Problems for University Exams (Short, Long, Coding & Case Study Questions)** This annexure contains exam-oriented questions designed for undergraduate, postgraduate, and professional certification evaluations. Questions range from basic conceptual understanding to advanced applications and coding tasks. A. Short Answer Questions (2–5 Marks Each) 1. What is a tensor in PyTorch? How is it different from NumPy arrays? 2. Define Autograd. Why is it important in deep learning? 3. What is the purpose of requires_grad=True in PyTorch tensors? 4. Explain the difference between CPU tensors and CUDA tensors. 5. What is a computational graph? 6. What does a PyTorch state_dict contain? 7. Def...