Are you looking for the Deep Learning IIT Ropar Week 4 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 4 assignment in the Deep Learning course offered by IIT Ropar.
Deep Learning IIT Ropar Week 4 Nptel Assignment Answers
Session: JULY – DEC 2024
Q1.A team has a data set that contains 1000 samples for training a feed-forward neural network. Suppose they decided to use stochastic gradient descent algorithm to update the weights. How many times do the weights get updated after training the network for 5 epochs? 1000 5000 100 5
Answer: B) 5000
Q2. What is the primary benefit of using Adagrad compared to other optimization algorithms? It converges faster than other optimization algorithms. It is more memory-efficient than other optimization algorithms. It is less sensitive to the choice of hyperparameters(learning rate). It is less likely to get stuck in local optima than other optimization algorithms.
Answer: It is more memory-efficient than other optimization algorithms.
These are Deep Learning IIT Ropar Week 4 Nptel Assignment Answers
Q3.What are the benefits of using stochastic gradient descent compared to vanilla gradient descent? SGD converges more quickly than vanilla gradient descent. SGD is computationally efficient for large datasets. SGD theoretically guarantees that the descent direction is optimal. SGD experiences less oscillation compared to vanilla gradient descent.
Answer:
Q4. Select the behaviour of the Gradient descent algorithm that uses the following update rule, wt+1=wt−η∇wt
where w is a weight and η is a learning rate. The weight update is tiny at a steep loss surface The weight update is tiny at a gentle loss surface The weight update is large at a steep loss surface The weight update is large at a gentle loss surface
Answer:The weight update is large at a steep loss surface
These are Deep Learning IIT Ropar Week 4 Nptel Assignment Answers
Q5.Given data where one column predominantly contains zero values, which algorithm should be used to achieve faster convergence and optimize the loss function? Adam NAG Momentum-based gradient descent Stochastic gradient descent
Answer: Adam
Q6. In Nesterov accelerated gradient descent, what step is performed before determining the update size? Increase the momentum Adjust the learning rate Decrease the step size Estimate the next position of the parameters
These are Deep Learning IIT Ropar Week 4 Nptel Assignment Answers
Q9.Which of the following algorithms will result in more oscillations of the parameter during the training process of the neural network? Stochastic gradient descent Mini batch gradient descent Batch gradient descent Batch NAG
Q10.Consider a gradient profile ∇W=[1,0.9,0.6,0.01,0.1,0.2,0.5,0.55,0.56]. Assume v−1=0,ϵ=0,β=0.9 and the learning rate is η−1=0.1 . Suppose that we use the Adagrad algorithm then what is the value of η6=η/sqrt(vt+ϵ)? 0.03 0.06 0.08 0.006
