CS 7643 Quiz 3 Exam 2026 Questions and
Answers
Modeling Error - Correct answer-Given a particular NN architecture, the actual
model that represents the real world may not be in that space.
When model complexity increases, modeling error reduces, but optimization error
increases.
Estimation Error - Correct answer-Even if finding the best hypothesis, weights, and
parameters that minimize training error, may not generalize to test set
Optimization Error - Correct answer-Even if your NN can perfectly model the
world, your algo may not find good weights that model the function.
When model complexity increases, modeling error reduces, but optimization error
increases.
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, Effectiveness of transfer learning under certain conditions - Correct answer-
Remove last FC layer of CNN and initialize it randomly, then run new data through
network to train only that layer
In order to train the NN for transfer learning -freeze the CNN layers or early layers
and learn parameters in the FC layers.
Performs very well on very small amount of training, if similar to the original data
Does not work very well if the target task's dataset is very different
If you have enough data in the target domain, and is different than the source,
better to just train on the new data
Transfer learning = reuse features we learn on a very large dataset on a completely
new thing
Steps:
Train on very large dataset
Take custom dataset and initialize network with weights trained in Step 1 (replace
last fully connected layer since classes in new network will be different)
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Answers
Modeling Error - Correct answer-Given a particular NN architecture, the actual
model that represents the real world may not be in that space.
When model complexity increases, modeling error reduces, but optimization error
increases.
Estimation Error - Correct answer-Even if finding the best hypothesis, weights, and
parameters that minimize training error, may not generalize to test set
Optimization Error - Correct answer-Even if your NN can perfectly model the
world, your algo may not find good weights that model the function.
When model complexity increases, modeling error reduces, but optimization error
increases.
©COPYRIGHT 2025, ALL RIGHTS RESERVED 1
, Effectiveness of transfer learning under certain conditions - Correct answer-
Remove last FC layer of CNN and initialize it randomly, then run new data through
network to train only that layer
In order to train the NN for transfer learning -freeze the CNN layers or early layers
and learn parameters in the FC layers.
Performs very well on very small amount of training, if similar to the original data
Does not work very well if the target task's dataset is very different
If you have enough data in the target domain, and is different than the source,
better to just train on the new data
Transfer learning = reuse features we learn on a very large dataset on a completely
new thing
Steps:
Train on very large dataset
Take custom dataset and initialize network with weights trained in Step 1 (replace
last fully connected layer since classes in new network will be different)
©COPYRIGHT 2025, ALL RIGHTS RESERVED 2
