Abstract
A picture is worth a thousand words. Sometimes a lot more. In many applications, the ability to see what a classification model is seeing is invaluable. This is especially true when the model is processing signals or images, which by nature have a visual representation. If the developer can study examples of the features that the model is associating with each class, this lucky developer may be clued in to strengths and weaknesses of the model. In this chapter, we will see how this can be done.
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© 2018 Timothy Masters
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Masters, T. (2018). Embedded Class Labels. In: Deep Belief Nets in C++ and CUDA C: Volume 2. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3646-8_1
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DOI: https://doi.org/10.1007/978-1-4842-3646-8_1
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-3645-1
Online ISBN: 978-1-4842-3646-8
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