Are you looking for the Deep Learning IIT Ropar Week 11 NPTEL Assignment Answers 2025 (jan-Apr)? You’ve come to the right place! Access the most accurate and up-to-date solutions for your Week 11 assignment in the Deep Learning course offered by IIT Ropar.
1) For which of the following problems are RNNs suitable?
a) Generating a description from a given image. b) Forecasting the weather for the next N days based on historical weather data. c) Converting a speech waveform into text. d) Identifying all objects in a given image.
2) Suppose that we need to develop an RNN model for sentiment classification. The input to the model is a sentence composed of five words, and the output is the sentiment (positive or negative). Assume that each word is represented as a vector of length 100×1, and the output labels are one-hot encoded. Further, the state vector st is initialized with all zeros of size 30×1. How many parameters (including bias) are there in the network?
a) GRUs have fewer parameters compared to LSTMs. b) GRUs use a single gate to control both input and forget mechanisms. c) GRUs are less effective than LSTMs in handling long-term dependencies. d) GRUs are a type of feedforward neural network.
a) They are simpler to implement. b) They solve the vanishing gradient problem. c) They require less computational power. d) They can handle non-sequential data.
Deep Learning IIT Ropar Week 11 Nptel Assignment Answers
6) What is the vanishing gradient problem in training RNNs?
a) The weights of the network converge to zero during training. b) The gradients used for weight updates become too large. c) The network becomes overfit to the training data. d) The gradients used for weight updates become too small.
7) What is the role of the forget gate in an LSTM network?
a) To determine how much of the current input should be added to the cell state. b) To determine how much of the previous time step’s cell state should be retained. c) To determine how much of the current cell state should be output. d) To determine how much of the current input should be output.
8) How does LSTM prevent the problem of vanishing gradients?
a) Different activation functions, such as ReLU, are used instead of sigmoid in LSTM. b) Gradients are normalized during backpropagation. c) The learning rate is increased in LSTM. d) Forget gates regulate the flow of gradients during backpropagation.
a) The gradients of Loss with respect to parameters are added across time steps. b) The gradients of Loss with respect to parameters are subtracted across time steps. c) The gradient may vanish or explode if timesteps are too large. d) The gradient may vanish or explode if timesteps are too small.
