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Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
book

Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python

by Umberto Michelucci
March 2022
Intermediate to advanced content levelIntermediate to advanced
397 pages
9h 6m
English
Apress
Content preview from Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
© Umberto Michelucci 2022
U. MichelucciApplied Deep Learning with TensorFlow 2https://doi.org/10.1007/978-1-4842-8020-1_9

9. Autoencoders

Umberto Michelucci1  
(1)
Dübendorf, Switzerland
 

In this chapter, we look at autoencoders. This chapter is a theoretical one, covering the mathematics and the fundamental concepts of autoencoders. We discuss what they are, what their limitations are, the typical use cases, and then look at some examples. We start with a general introduction to autoencoders, and we discuss the role of the activation function in the output layer and the loss function. We then discuss what the reconstruction error is. Finally, we look at typical applications, such as dimensionality reduction, classification, denoising, and anomaly ...

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Publisher Resources

ISBN: 9781484280201Purchase LinkPublisher Website