CS 7643 QUIZ 3 QUESTIONS AND
CORRECT DETAILED ANSWERS|| NEWEST
UPDATE 2025|ALREADY GRADED A+.
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.
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)
Final step -> continue training on new dataset
CORRECT DETAILED ANSWERS|| NEWEST
UPDATE 2025|ALREADY GRADED A+.
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.
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)
Final step -> continue training on new dataset
