STUDY OF ARTIFICIAL NEURAL NETWORK
A subfield of machine learning is Artificial Neural Networks (ANN). This algorithm is based on the neural network of the human brain. An Artificial Neural Network is made up of a number of connected nodes that act as information carriers. Recently, artificial neural networks with artificial neurons that behave and perform like actual neurons were introduced. They are employed for a variety of tasks that a human brain may perform, including speech, hearing, reorganization, pattern-spotting, and storing knowledge. This is not the case for any artificial networking because these neural networks were combined and dynamically self-assembled. To tackle the issue, these neurons cooperate in clusters and divide it into smaller components. Engineering skill is required to make them, which are arranged in layers, solve issues in the actual world. The connections between the neurons are crucial because they serve as the system's glue during the excitation-inhibition process, in which one neuron is excited while the other is inhibited as in subtraction-addition operations. During training, this network controls the connections. The issues are identified and resolved by this ANN. This paper provides an overview of an Artificial Neural Network. Additionally, applications and types are explained in this research article.