February 2019
Intermediate to advanced
386 pages
9h 54m
English
Content preview from Hands-On Unsupervised Learning with Python
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Generative Adversarial Networks and SOMs
In this chapter, we will finish our journey through the world of unsupervised learning, discussing some very popular neural models that can be employed to perform a data generating process and new samples that can be drawn from it. Moreover, we will analyze the functionality of self-organizing maps, which can adapt their structures so that specific units become responsive to distinct input patterns.
In particular, we will discuss the following topics:
- Generative adversarial networks (GANs)
- Deep convolutional GANs (DCGANs)
- Wasserstein GANs (WGANs)
- Self-organizing maps (SOMs)