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The data representations describe the design of transit data models.
We outline and discuss the diverse available options in optimizing the data representations used.
The data representations in the hidden layer present meaningful abstraction levels for the analysis of the learning performed.
Earlier, we discussed some studies that utilize the data representations extracted through Deep Learning for semantic indexing.
But (ON), and hence GDI, does not specify whether, ultimately, the occurrence of every discrete state necessarily requires a material implementation of the data representations.
Using Deep Learning algorithms can help us to extract semantic features from a massive amount of text data in addition to reduce dimensions of the data representations.
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The drawing of the data representation is done by a Data Representation.
Most of the functionality of the data representation is implemented in this base class.
The boundaries and limits for the data representation has to be very explicit.
The former method is fast to sample, yet sacrifices the accuracy of the data representation.
In the first part of this paper, we describe the data representation.
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