Error versus Weight Updates (Example 1) 0.01 Training set error Validation set error 0.009 0.008 0.007 0.006 0.005 0.004 0.003 0.002 5000 10000 Number of weight updates 20000 15000 Is there overfitting with the trained model? How do you know?

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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5. Consider below training error and test error observed as we train for a neural network using full (i.e.,
batch) gradient descent.
Error versus Weight Updates (Example 1)
0.01
Training set error
Validation set error
0.009
0.008
0.007
0.006
0.005
0.004
0.003
0.002
5000
10000
15000
20000
Number of weight updates
1)
Is there overfitting with the trained model? How do you know?
2)
List several possible causes of overfitting if it does exist?
3)
error and testing error, respectively.
If we double the size of the training data, plot the new curves (on the figure) for training
Briefly explain why we may have such new curves.
Error
Transcribed Image Text:5. Consider below training error and test error observed as we train for a neural network using full (i.e., batch) gradient descent. Error versus Weight Updates (Example 1) 0.01 Training set error Validation set error 0.009 0.008 0.007 0.006 0.005 0.004 0.003 0.002 5000 10000 15000 20000 Number of weight updates 1) Is there overfitting with the trained model? How do you know? 2) List several possible causes of overfitting if it does exist? 3) error and testing error, respectively. If we double the size of the training data, plot the new curves (on the figure) for training Briefly explain why we may have such new curves. Error
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