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CS 7643 QUIZ 4 2026 QUESTIONS
AND ANSWERS | A+ GRADED | WITH
EXPERT SOLUTIONS
Recurrent Neural Network (RNN) - correct answer -A 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 answer -the 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
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how do RNN and LSTM update rules differ? - correct answer -
LSTM 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 answer -The 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 answer -t-Distributed
Stochastic Neighbor Embedding
what is t-SNE? - correct answer -is an unsupervised non-linear
technique primarily used for data exploration and visualization of
high dimensional data. In short, it gives you a feel or intuition of
how data is in high dimensional space.
How does t-SNE conceptually work? - correct answer -The
algorithm calculates a similarity measure between pairs of
CS 7643 QUIZ 4 2026 QUESTIONS
AND ANSWERS | A+ GRADED | WITH
EXPERT SOLUTIONS
Recurrent Neural Network (RNN) - correct answer -A 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 answer -the 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
, Page | 2
how do RNN and LSTM update rules differ? - correct answer -
LSTM 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 answer -The 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 answer -t-Distributed
Stochastic Neighbor Embedding
what is t-SNE? - correct answer -is an unsupervised non-linear
technique primarily used for data exploration and visualization of
high dimensional data. In short, it gives you a feel or intuition of
how data is in high dimensional space.
How does t-SNE conceptually work? - correct answer -The
algorithm calculates a similarity measure between pairs of
