Consider the task of applying a convolutional layer with a stride of 1 to an image of size 227 × 227 (Grayscale), while ensuring that the output feature map retains the same spatial dimensions. Which of the following code snippets correctly implements this using PyTorch?
Output size: 8 × 8; Effective receptive field: 5 × 5
Output size: 10 × 10; Effective receptive field: 5 × 5
Output size: 10 × 10; Effective receptive field: 3 × 3
Output size: 12 × 12; Effective receptive field: 7 × 7
You are given the following CNN architectures:
Which of the following correctly orders these networks in terms of their increasing depth (i.e., from the fewest to the greatest number of layers)?
AlexNet < InceptionNet-v3 < VGG-19 < ResNet-50
AlexNet < VGG-19 < Inception-v3 < ResNet-50
Inception-v3 < AlexNet < VGG-19 < ResNet-50
AlexNet < Inception-v3 < ResNet-50 < VGG-19
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The IIT Madras BS Deep Learning Practice (Deep Learning Practice) End Term paper sat on 31 Aug 2025, in the May 2025 term, set QDB1: 22 questions for 100 marks in 180 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.
| Feature | Deep Learning Practice End Term 31 Aug 2025 Set QDB1 at a glance |
|---|---|
| Term | May 2025 term |
| Subject | Deep Learning Practice |
| Course code | BSDA5013 |
| Questions | 22 |
| Marks | 100 |
| Duration | 180 min |
| MCQ | 7 |
| MSQ | 8 |
| Numerical | 7 |
| Official paper | IIT M DEGREE AN EXAM QDB3 31 Aug 2025 |
| Negative marking | No negative marking. |
| Updated |
