Deep been collected with statistical purposes. Statistical curves

Deep learning is an emerging area of machine learning research. It
comprises of multiple hidden layers of artificial neural networks. The deep
learning methodology applies non linear transformations and model abstractions
of high level in large databases. The recent development in deep learning
architectures within numerous fields have already provided significant contributions
in artificial intelligence.

 

 

Artificial intelligence (AI) as an intelligence exhibited by
machines has been an effective approach to human learning and reasoning 1. In
1950, “The Turing Test” was proposed as a satisfactory explanation of how a
computer could perform a human cognitive reasoning 2. As a research field, AI
is divided in more specific research sub-fields. For example: Natural Language
Processing (NLP) 3 can enhance the writing experience in various applications
4,5. The most classic subdivision within NLP is machine translation, which is
understood as the translation between languages. Machine translation algorithms
have resulted in various applications that consider grammar structure as well
as spelling mistakes. Moreover, a set of words and vocabulary related to the
main topic is automatically used as the main source when the computer is
suggesting changes to writer or editor 6. Fig. 1 shows in detail how AI
covers seven subfields of computer sciences.

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Recently, machine learning and data mining have become the center
of attention and the most popular topics among researchers. These combined
fields of study analyze multiple possibilities of characterization of databases 7.
Through the years, databases have been collected with statistical purposes.
Statistical curves can describe past, and present in order to predict future
behaviors. Nevertheless, during the last decades only classic techniques and
algorithms have been used to process this data, whereas an optimization of
those algorithms could lead on an effective self–learning 8. A better
decision making can be implemented based on existing values, multiple criteria
and statistics advanced methods.

 

 

 

Figure 1.  Research in artificial
intelligence and subfields.

 

Since ML covers a wide range of research, many approaches have
been established. Clustering, Bayesian Network, Deep Learning and Decision Tree
Learning are only part of the approaches. The following review mainly focuses
on deep learning, its basic concepts, past and nowadays applications in
different fields. Additionally, it presents several figures portraying the
rapid development of deep learning research through publications over the
recent years in scientific databases.

 

 

 

 

 

 

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