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Regularization for Deep Learning

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Regularization for Deep Learning C. Lee Giles Thanks to Alexander Ororbia and Sargur Srihari
What is Regularization?
Goals of Regularization
Regularize Your Estimator! regularizer
Regularization & Model Types
The Value of Regularization Model family
Finding the Best Model

L1 and L2 Unit Balls Unit ball = set of points equidistant to origin Goal = find a way to get to the red line

Dead unit = neuron will never activate on any datapoint again

Augment the data! More robust to adversarial examples

Data augmentation process can be dynamic!

: : regularization

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Assessment for Deep Learning

Assessment for Deep Learning. Dr. Julian Hermida Algoma University Workshop on Teaching and Learning February 25, 2009. Agenda. Objective: Student assessment that promotes deep learning. Interactive talk: Review of main concepts. Assessment and deep learning.

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