January 2018
Intermediate to advanced
470 pages
11h 9m
English
Content preview from Scala Machine Learning Projects
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Efficient data representation with autoencoders
A big problem that plagues all supervised learning systems is the so-called curse of dimensionality: a progressive decline in performance while increasing the input space dimension. This occurs because the number of necessary samples to obtain a sufficient sampling of the input space increases exponentially with the number of dimensions. To overcome these problems, some optimizing networks have been developed.
The first are autoencoders networks: these are designed and trained for transforming an input pattern in itself, so that, in the presence of a degraded or incomplete version of an input pattern, it is possible to obtain the original pattern. The network is trained to create output data ...
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