CS-7643 QUIZ 4 EXAM WITH CORRECT
QUESTIONS AND ANSWERS 2025
Recurrent Neural Network (RNN) - CORRECT-ANSWERSA RNN models sequential
interactions through a hidden state, or memory. It can take up to N inputs and produce up to
N outputs. For example, an input sequence may be a sentence with the outputs being the
part-of-speech tag for each word (N-to-N). An input could be a sentence, and the output a
sentiment classification of the sentence (N-to-1). An input could be a single image, and the
output could be a sequence of words corresponding to the description of an image (1-to-N).
At each time step, an RNN calculates a new hidden state ("memory") based on the current
input and the previous hidden state. The "recurrent" stems from the facts that at each step
the same parameters are used and the network performs the same calculations based on
different inputs
, LSTM (Long Short-Term Memory) - CORRECT-ANSWERSthe network was invented to
prevent the vanishing gradient problem in Recurrent Neural Networks by using a memory
gating mechanism. Using LSTM units to calculate the hidden state in an RNN we help to the
network to efficiently propagate gradients and learn long-range dependencies
how do RNN and LSTM update rules differ? - CORRECT-ANSWERSLSTM networks update
rule is cell state is updated in an additive way by adding something to its previous value C_t-1,
this differs from the multiplicative update Rule of RNN.
Gradients for RNNs - CORRECT-ANSWERSThe gradient computation involves recurrent
multiplication of WW. This multiplying by WW to each cell has a bad effect. Think like this: If
you a scalar (number) and you multiply gradients by it over and over again for say 100 times,
if that number > 1, it'll explode the gradient and if < 1, it'll vanish towards 0.
What does t-SNE stand for? - CORRECT-ANSWERSt-Distributed Stochastic Neighbor
Embedding
QUESTIONS AND ANSWERS 2025
Recurrent Neural Network (RNN) - CORRECT-ANSWERSA RNN models sequential
interactions through a hidden state, or memory. It can take up to N inputs and produce up to
N outputs. For example, an input sequence may be a sentence with the outputs being the
part-of-speech tag for each word (N-to-N). An input could be a sentence, and the output a
sentiment classification of the sentence (N-to-1). An input could be a single image, and the
output could be a sequence of words corresponding to the description of an image (1-to-N).
At each time step, an RNN calculates a new hidden state ("memory") based on the current
input and the previous hidden state. The "recurrent" stems from the facts that at each step
the same parameters are used and the network performs the same calculations based on
different inputs
, LSTM (Long Short-Term Memory) - CORRECT-ANSWERSthe network was invented to
prevent the vanishing gradient problem in Recurrent Neural Networks by using a memory
gating mechanism. Using LSTM units to calculate the hidden state in an RNN we help to the
network to efficiently propagate gradients and learn long-range dependencies
how do RNN and LSTM update rules differ? - CORRECT-ANSWERSLSTM networks update
rule is cell state is updated in an additive way by adding something to its previous value C_t-1,
this differs from the multiplicative update Rule of RNN.
Gradients for RNNs - CORRECT-ANSWERSThe gradient computation involves recurrent
multiplication of WW. This multiplying by WW to each cell has a bad effect. Think like this: If
you a scalar (number) and you multiply gradients by it over and over again for say 100 times,
if that number > 1, it'll explode the gradient and if < 1, it'll vanish towards 0.
What does t-SNE stand for? - CORRECT-ANSWERSt-Distributed Stochastic Neighbor
Embedding
