Chapter 5. Optimizing TensorFlow Autoencoders
In Machine Learning (ML), the so-called curse of dimensionality is a progressive decline in performance with an increase in the input space, often with hundreds or thousands of dimensions, which does not occur in low-dimensional settings such as three-dimensional space. This occurs because the number of samples needed to obtain a sufficient sampling of the input space increases exponentially with the number of dimensions. To overcome this problem, some optimizing networks have been developed.
The first one is autoencoder networks. These are designed and trained to transform 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. An autoencoder is a Neural Network (NN). The network is trained to create output data like those presented in the entrance and the hidden layer stores the compressed data.
The second optimizing networks are Boltzmann...
