CS 7643 Quiz 3 Exam LATEST 2025 UPDATE
WITH COMPLETE QUESTIONS AND CORRECTLY
WELL DEFINED ANSWERS 100%GUARANTEED
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Terms in this set (39)
Given a particular NN architecture, the actual model
that represents the real world may not be in that
space.
Modeling Error
When model complexity increases, modeling error
reduces, but optimization error increases.
Even if finding the best hypothesis, weights, and
Estimation Error parameters that minimize training error, may not
generalize to test set
Even if your NN can perfectly model the world, your
algo may not find good weights that model the
function.
Optimization Error
When model complexity increases, modeling error
reduces, but optimization error increases.
, 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
Effectiveness of transfer
learning under certain
conditions
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
Can either retrain all weights ("finetune") or freeze
(ie: not update) weights in certain layers (freezing
reduces number of parameters that you need to
learn)
WITH COMPLETE QUESTIONS AND CORRECTLY
WELL DEFINED ANSWERS 100%GUARANTEED
PASS!!!
Save
Terms in this set (39)
Given a particular NN architecture, the actual model
that represents the real world may not be in that
space.
Modeling Error
When model complexity increases, modeling error
reduces, but optimization error increases.
Even if finding the best hypothesis, weights, and
Estimation Error parameters that minimize training error, may not
generalize to test set
Even if your NN can perfectly model the world, your
algo may not find good weights that model the
function.
Optimization Error
When model complexity increases, modeling error
reduces, but optimization error increases.
, 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
Effectiveness of transfer
learning under certain
conditions
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
Can either retrain all weights ("finetune") or freeze
(ie: not update) weights in certain layers (freezing
reduces number of parameters that you need to
learn)
