Consider the following data point and weight vector in a simple single layer neural network. (Assume that there is no bias.) x = [1,0] w = [0,1] Once the data flows through the simple network and is passed through the activation function, the final output comes out to be 0.5. Once you have identified the activation function, enter the maximum value that the derivative of the activation function can take.
A BatchNorm layer is applied to the output of a convolutional layer. The output feature map has dimensions: 
where: 32 = Batch size (number of images in the mini-batch)• 28 = Height of each feature map• 28 = Width of each feature map• 64 = Number of channels• How many learnable parameters does this BatchNorm layer contain?
Consider a McCulloch-Pitts (MP) neuron with binary inputs, unit positive weights, and no inhibitory connections. Given the following data points: a = [0,0] b = [1,1] c = [1,0] The neuron must satisfy: a → 0 b → 0 c → 1 Which of the following statements is TRUE (θ is the threshold for the neuron to fire.)?
This is possible with 
= 1
This is possible with = 2
This is possible with some threshold value
This is impossible for a standard MP neuron
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The IIT Madras BS Introduction to Deep Learning and Generative AI (Deep Learning and GenAI) Quiz 1 paper sat on 19 Jul 2026, in the May 2026 term: 19 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 1 19 Jul 2026 at a glance |
|---|---|
| Term | May 2026 term |
| Subject | Introduction to Deep Learning and Generative AI |
| Course code | BSDA2001 |
| Questions | 19 |
| Marks | 50 |
| Duration | 120 min |
| Numerical | 4 |
| MCQ | 15 |
| Official paper | Introduction To Deep Learning And Generative Ai 16 Jul 26 |
| Negative marking | No negative marking. |
| Updated |
