What is artificial neural networks (ANN)? And what is the difference and similarity between artificial intelligence (AI), machine learning (ML), deep learning (DL) and artificial neural networks (ANN)?
Artificial Neural Network (ANN) is a term used to describe algorithms and programs that mimic or emulate the way biological brains process information. Artificial neural networks simulate biological networks. Just as a person gains muscle memory, the system will store and ‘learn’ from all its past data. Within this framework, there are both shallow and deep experiential networks. Furthermore, ANN is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes or learns based on input and output. The information provided to the system will employ various models of machine learning (ML) to conduct different solutions. Then the system will correlate the ‘experiences’ to come up with an outcome.
The artificial neural network (ANN) was initially inspired by the form and functions of biological neurons. Biological brains are capable of solving difficult problems, but each neuron is only responsible for solving a very small part of the problem. Similarly, an ANN is made up of cells or smaller algorithms in our case that work together to produce a desired result, although each individual cell is only responsible for solving a small part of the problem. When combined, the problem is hopefully resolved. An ANN typically requires millions or billions of computations to perform well, thus requiring a larger infrastructure.
The difference between machine learning, deep leaning, artificial intelligence (AI) and artificial neural networks is explained briefly as follows:
- Artificial Intelligence (AI) is a technique that enables computers to mimic human intelligence, using logic, decision trees and machine learning (ML) algorithms.
- Machine learning (ML) is a subset of Artificial Intelligence (AI) that includes statistical techniques that enable machines to improve at tasks with experience.
- Deep learning (DL) is the subset of machine learning (ML) composed of algorithms that permit software to train itself to perform tasks by exposing multilayered nodes to process vast amounts of data. In addition, deep learning is taking machine learning and adding more nodes or areas of complexity.
- Artificial Neural Network (ANN) is a concept used to describe algorithms and programs that mimic biological neural networks. The machine learns based on input and output as information flows through these networks.