Figure from the original question paper

(A) stacked_x_dim0: [3, 3], stacked_x_dim1: [4, 4]

(B) stacked_x_dim0: [4, 3], stacked_x_dim1: [3, 4]

(C) stacked_x_dim0: [3, 4], stacked_x_dim1: [4, 3]

(D) stacked_x_dim0: [4, 4], stacked_x_dim1: [3, 3]

Figure from the original question paper

(A) It will successfully convert gpu_tensor to a NumPy array on the CPU.

(B) It will successfully convert gpu_tensor to a NumPy array that also resides on the GPU.

(C) It will raise a RuntimeError because NumPy arrays cannot directly operate on GPU memory.

(D) It will create a view of the gpu_tensor on the CPU without copying data.

(E) The numpy() method is not available for GPU tensors.

Deep Learning and GenAI Quiz 2: 23 November 2025 (September 2025 term)

Question 1

+2 marksOne correct option

A
stacked_x_dim0: [3, 3], stacked_x_dim1: [4, 4]
B
stacked_x_dim0: [4, 3], stacked_x_dim1: [3, 4]
C
stacked_x_dim0: [3, 4], stacked_x_dim1: [4, 3]
D
stacked_x_dim0: [4, 4], stacked_x_dim1: [3, 3]

Question 2

+1 markOne correct option

A
It will successfully convert gpu_tensor to a NumPy array on the CPU.
B
It will successfully convert gpu_tensor to a NumPy array that also resides on the GPU.
C
It will raise a RuntimeError because NumPy arrays cannot directly operate on GPU memory.
D
It will create a view of the gpu_tensor on the CPU without copying data.
E
The numpy() method is not available for GPU tensors.

Question 3

+1 markOne correct option

The Huber Loss function is introduced as a robust alternative to Mean Squared Error (MSE) and Mean Absolute Error (MAE). What is the primary characteristic of Huber Loss that makes it robust to outliers?

A
It always produces a gradient of zero for all errors, preventing large updates.
B
It exclusively uses a quadratic penalty, but only for errors exactly at the threshold.
C
It transitions from a quadratic loss for small errors to a linear loss for large errors, thereby down-weighting the impact of outliers.
D
It entirely ignores errors beyond a certain delta, considering them irrelevant.
E
It applies a logarithmic transformation to all error values before computing the loss.

22 more questions in this paper

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More on the Deep Learning and GenAI Quiz 2 23 Nov 2025 paper

The IIT Madras BS Introduction to Deep Learning and Generative AI (Deep Learning and GenAI) Quiz 2 paper sat on 23 Nov 2025, in the September 2025 term: 25 questions for 50 marks in 120 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 and GenAI Quiz 2 23 Nov 2025 at a glance
Term	September 2025 term
Subject	Introduction to Deep Learning and Generative AI
Course code	BSDA2001
Questions	25
Marks	50
Duration	120 min
MCQ	17
Numerical	7
MSQ	1
Official paper	IIT M DIPLOMA AN EXAM QDD2 23 Nov 2025 NEW
Negative marking	No negative marking.
Updated	29 Sept 2026