To begin getting a profit with Neuroseed you have to be brought up to date
Today we are discussing the most fast-paced branch of science and 3 main topics that form the whole AI structure. They all are interrelated and should be learned to start working with the Neuroseed ecosystem. So, let’s begin with the definition.
Neural networks are one of the research areas in the field of artificial intelligence, based on attempts to reproduce the human nervous system. Namely, the ability of the nervous system to learn and correct mistakes allows simulating the work of the human brain. It is a complex human structures network that ensures the interconnected behavior of all body systems. Having considered the definition we should discuss its main element.
Well, you have got acquainted with the meaning of the topic main body and we are turning to the next stage.
Machine Learning is an extensive subsection of Artificial Intelligence that studies methods of constructing algorithms capable of learning. There are two types of learning:
- Learning by use of precedents, or inductive learning.
- Deductive learning involves formalizing the knowledge of experts and transferring them to the computer as a database. It is usually referred to the field of expert systems, so the precedents terms machine learning can be considered synonymous.
Machine Learning is at the intersection of mathematical statistics, optimization methods, and classical math disciplines, but it also has own specifics associated with problems of computational efficiency. Many methods of inductive learning were developed as an alternative to classical statistical approaches. They are closely related to information extraction and data mining.
The most theoretical sections of machine learning are combined in a separate direction, called the computational learning theory (COLT).
ML is not only mathematical but also practical, and engineering discipline. Actually, a pure theory does not immediately lead to methods and algorithms applicable in practice. To make them work well, we have to invent additional heuristics that balance between the theory assumption inconsistency and the real problem conditions. As you see, first two topics are closely connected and depend on each other. Let’s talk about the last one.
So what is deep learning?
This term describes a specific approach to neural network teaching. First, neural networks accept an array of numbers (pixels, audio signal, or words), perform some actions, and then return the numbers.
The neural network memory is an array of numbers, that determine how the input data is combined to get the result.
The biggest obstacle to using neural networks is how to set all the arrays of values that would effectively perform the task of converting input signals into output forecasts.
To make a conclusion, Neuroseed combines all the features to perform the most effective result in the market. All mentioned topics are connected so you should define and differentiate them.
