The “neural” part of the term refers to the initial inspiration of the concept - the structure of the human brain.
Conceptually, the way ANN operates is indeed reminiscent of the brainwork, albeit in a very purpose-limited form.
The thing is - Neural Network is not some approximation of the human perception that can understand data more efficiently than human - it is much simpler, a specialized tool with algorithms designed to achieve specific results.
The key goals of using MLP in the data processing and analysis operation are:
Study the data and explore the nuances of its structure;
Train the model on the representative dataset;
If previously, the information was a commodity with a value limited to its instantly accessible features, now it is a resource the value of which depends on one’s skill to interpret it - the ability to make the most out of the available information.
This process requires complex systems that consist of multiple layers of algorithms, that together construct a network inspired by the way the human brain works, hence its name - neural networks.
Vector is an abstract representation of raw data that reiterates its meaning into a comprehensive form for the machine.
Unlike other types of neural networks that process data straight, where each element is processed independently of the others, recurrent neural networks keep in mind the relations between different segments of data, in more general terms, context.
Just like traditional Artificial Neural Networks, RNN consists of nodes with three distinct layers representing different stages of the operation.
Overall, the RNN neural network operation can be one of the three types:
According to a research report "Artificial Neural Network Market by Component (Solutions, Platform/API and Services), Application (Image Recognition, Signal Recognition, and Data Mining), Deployment Mode, Organization Size, Industry Vertical, and Region - Global Forecast to 2024", published by MarketsandMarkets, the global ANN market size is expected to grow from USD 117 million in 2019 to USD 296 million by 2024, at a Compound Annual Growth Rate (CAGR) of 20.5% during the forecast period.
The data mining segment is expected to grow at a rapid pace in the coming years in the ANN market.
It is one of the leading applications in the ANN market due to growing demand to extract hidden predictive information from huge databases.Among services, the consulting services segment to grow at a higher CAGR during the forecast periodThe ANN market is segmented based on services into two categories, namely, managed services and professional services.
The professional services is further bifurcated into consulting services, support and maintenance services and deployment and integration.
The consulting services segment is expected to grow at a rapid pace during the forecast period, as consulting services help organizations to utilize ANN tools capabilities that uses graph structures for semantic queries with nodes, edges, and properties to represent and store connected data.
Currently servicing 7500 customers worldwide including 80% of global Fortune 1000 companies as clients.