Deep Learning IIT Ropar Week 8 Nptel Assignment Answers
Are you looking for the Deep Learning IIT Ropar Week 8 NPTEL Assignment Answers 2024 (July-Dec)? You’ve come to the right place! Access the most accurate and up-to-date solutions for your Week 8 assignment in the Deep Learning course offered by IIT Ropar.
Which of the following activation functions is not zero-centered? A) Sigmoid B) Tanh C) ReLU D) Softmax
Answer: C) ReLU
What is the gradient of the sigmoid function at saturation? Answer: 0
Given a neuron initialized with weights w1=1.5, w2=0.5, and inputs x1=0.2, x2=−0.5, calculate the output of a ReLU neuron. Answer: 0.05
How does pre-training prevent overfitting in deep networks? A) It adds regularization B) It initializes the weights near local minima C) It constrains the weights to a certain region D) It eliminates the need for fine-tuning
Answer: D) It eliminates the need for fine-tuning
We train a feed-forward neural network and notice that all the weights for a particular neuron are equal. What could be the possible causes of this issue? A) Weights were initialized randomly B) Weights were initialized to high values C) Weights were initialized to equal values D) Weights were initialized to zero
Answer: A) Weights were initialized randomly C) Weights were initialized to equal values
Which of the following best describes the concept of saturation in deep learning? A) When the activation function output approaches either 0 or 1 and the gradient is close to zero. B) When the activation function output is very small and the gradient is close to zero. C) When the activation function output is very large and the gradient is close to zero. D) None of the above.
Answer: A) When the activation function output approaches either 0 or 1 and the gradient is close to zero.
These are Deep Learning IIT Ropar Week 8 Nptel Assignment Answers
Which of the following is true about the role of unsupervised pre-training in deep learning? A) It is used to replace the need for labeled data B) It is used to initialize the weights of a deep neural network C) It is used to fine-tune a pre-trained model D) It is only useful for small datasets
Answer: B) It is used to initialize the weights of a deep neural network
Which of the following is an advantage of unsupervised pre-training in deep learning? A) It helps in reducing overfitting B) Pre-trained models converge faster C) It improves the accuracy of the model D) It requires fewer computational resources
Answer: B) Pre-trained models converge faster
What is the main cause of the Dead ReLU problem in deep learning? A) High variance B) High negative bias C) Overfitting D) Underfitting
Answer: B) High negative bias
What is the main cause of the symmetry breaking problem in deep learning? A) High variance B) High bias C) Overfitting D) Equal initialization of weights
Answer: D) Equal initialization of weights
These are Deep Learning IIT Ropar Week 8 Nptel Assignment Answers
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Deep Learning Week 8 Nptel Assignment Answers (Apr-Jun 2023)
Q1. Which of the following functions can be used as an activation function in the output layer if we wish to predict the probabilities of n classes such that the sum of p over all n equals to 1? a. Softmax b. RelU c. Sigmoid d. Tanh
Answer: a. Softmax
Q2. The input image has been converted into a matrix of size 256 X 256 and a kernel/filter of size 5×5 with a stride of 1 and no padding. What will be the size of the convoluted matrix? a. 252×252 b. 3×3 c 254×254 d. 256×256
These are NPTEL Deep Learning Week 8 Assignment Answers
Q3. What will be the range of output if we apply ReLU non-linearity and then Sigmoid Nonlinearity subsequently after a convolution layer? a. [1,1] b. [0,1] c. [0.5,1] d. [1,-0.5]
Answer: c. [0.5,1]
Q4. The figure below shows image of a face which is input to a convolutional neural net and the other three images shows different levels of features extracted from the network. Can you identify from the following options which one is correct?
a. Label 3: Low-level features, Label 2: High-level features, Label 1: Mid-level features b. Label 1: Low-level features, Label 3: High-level features, Label 2: Mid-level features c. Label 2: Low-level features, Label 1: High-level features, Label 3: Mid-level features d. Label 3: Low-level features, Label 1: High-level features, Label 2: Mid-level features
Answer: b. Label 1: Low-level features, Label 3: High-level features, Label 2: Mid-level features
These are NPTEL Deep Learning Week 8 Assignment Answers
Q5. Suppose you have 8 convolutional kernel of size 5 x 5 with no padding and stride 1 in the first layer of a convolutional neural network. You pass an input of dimension 228 x 228 x 3 through athis layer. What are the dimensions of the data which the next layer will receive? a. 224x224x3 b. 224x224x8 c. 226x226x8 d. 225x225x3
Answer: b. 224x224x8
Q6. What is the mathematical form of the Leaky RelU layer? a. f(x)=max(0,x) b. f(x)=min(0,x) c. f(x)=min(0, ax), where a is a small constant d. f(x)=1(x<0)(ax)+1(x>=0)(x), where a is a small constant
Answer: d. f(x)=1(x<0)(ax)+1(x>=0)(x), where a is a small constant
These are NPTEL Deep Learning Week 8 Assignment Answers
Q7. The input image has been converted into a matrix of size 224 x 224 and convolved with a kernel/filter of size FxF with a stride of s and padding P to produce a feature map of dimension 222×222. Which among the following is true? a. F=3×3,s=1,P=1 b. F=3×3,s=0, P=1 c. F=3×3,s=1,P=0 d. F=2×2,s=0, P=0
These are NPTEL Deep Learning Week 8 Assignment Answers
Q8. Statement 1: For a transfer learning task, lower layers are more generally transferred to another task Statement 2: For a transfer learning task, last few layers are more generally transferred to another task Which of the following option is correct? a. Statement 1 is correct and Statement 2 is incorrect b. Statement 1 is incorrect and Statement 2 is correct c. Both Statement 1 and Statement 2 are correct d. Both Statement 1 and Statement 2 are incorrect
Answer: a. Statement 1 is correct and Statement 2 is incorrect
These are NPTEL Deep Learning Week 8 Assignment Answers
Q9. Statement 1: Adding more hidden layers will solve the vanishing gradient problem for a 2-layer neural network Statement 2: Making the network deeper will increase the chance of vanishing gradients. a. Statement 1 is correct b. Statement 2 is correct c. Neither Statement 1 nor Statement 2 is correct d. Vanishing gradient problem is independent of number of hidden layers of the neural network.
Answer: b. Statement 2 is correct
These are NPTEL Deep Learning Week 8 Assignment Answers
Q10. How many convolution layers are there in a LeNet-5 architecture? a. 2 b. 3 c 4 d. 5
Answer: a. 2
These are NPTEL Deep Learning Week 8 Assignment Answers
