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: