Machine Learning and Imaging Lecture 10, Part 2: Ingredients of a Convolutional Neural Network

In this lecture, Dr. Horstmeyer continues to describe the basic components of a convolutional neural network (CNN). He analyzes the dimensionality of data and weights within each layer, presents details regarding convolutional strides and pooling operations, and overviews the various non-linear functions available for weight activation. He additionally discusses regularization strategies and presents an overview of available loss minimization strategies, which will be continued in Lecture 10, Part 3. Additional resources are available at deepimaging.github.io
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