A neural network represents an emerging technology that has been rooted in several disciplines.

A neural network represents an emerging technology that has been rooted in several disciplines.

They are endowed with unique attributes such as the ability to adapt and learn from the environment, and approximate complicated images and mappings. Historically, the first neural network was developed for the implementation of a non-threshold Boolean function. With the advancing technology in a digital computer, the work of neural networks has been motivated by their interception by the recognition that human brains work in an entirely different way. The human brain can now be termed as a parallel, high complex nonlinear computer that can organize neutrons on the computation of motor control, pattern recognition, and perceptions.

The interconnection between neurons has made the human brain to work faster than the first digital computer. Ideally, a neutral network obtains computing power through either massive parallel distribution on the structure or from its ability to learn and generalize. Neural networks represent an emerging technology rooted in many disciplines. This popular and important area of science and technology was extensively developing for a recent period of time. Neural networks are endowed with some unique attributes, like the ability to learn from and adapt to their environment and the ability to approximate very complicated mappings.

Contributions of CNN and ANN

The transition of the neural network to deep learning algorithms from machine learning algorithms has profoundly impacted how human interaction with the world. Convolutional Neural Network (CNN), Artificial Neutral Network (ANN), and Recurrent Neutral Network (RNN) are the cores of a deep learning revolution that now offer powering applications. CNN models are currently used in various forms and domains as they are prevalent in video and image processing projects. It captures spatial features from images, learns filters automation to extract relevant and right elements from the input data, and follows the parameter sharing concept.

On the other hand, CNN constitutes three layers (input, hidden, and output), where each layer learns certain weights. They can learn any nonlinear function through the interpretation of any weights that map inputs to outputs.