Artificial Neural Networks

Artificial neural networks (ANNs) mimic the neural networks of human brain. Neural networks consist of node layers, in which there is an input layer, a hidden layer, activation function and an output layer. Each node can be referred to as an artificial neuron and is composed of input data, weights, biases, and output. 

Artificial neural networks (ANNs) is a data processing and output generation system in which the neural system is replicated to unravel non-linear relations in a large dataset. The data might be in the form of text, pictures, or audio and it might come from sensory routes.

Artificial neural networks, also known as neural networks is often used synonymously with deep learning. However, , deep learning refers to training artificial neural networks, technically. ANNs are a subset of machine learning (ML), which is a branch of artificial intelligence (AI) and computer science that requires collecting large amounts of data and use algorithms to help the machine learn like the human brain. 

Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs) are two main type of artificial neural networks.

Artificial neural networks allows businesses to stay agile and adapt to market changes, improves logistics and business functioning, offers robust user analysis for marketing and targeting and assists with medical imaging and diagnosis.