Understanding Neural Network: A beginner’s guide

Neural network or artificial neural network is one of the frequently used buzzwords in analytics these days. Neural network is a machine learning technique which enables a computer to learn from the observational data. Neural network in computing is inspired by the way biological nervous system process information.

Biological neural networks consist of interconnected neurons with dendrites that receive inputs. Based on these inputs, they produce an output through an axon to another neuron.

The term “neural network” is derived from the work of a neuroscientist, Warren S. McCulloch and Walter Pitts, a logician, who developed the first conceptual model of an artificial neural network. In their work, they describe the concept of a neuron, a single cell living in a network of cells that receives inputs, processes those inputs, and generates an output.

In the computing world, neural networks are organized on layers made up of interconnected nodes which contain an activation function. These patterns are presented to the network through the input layer which further communicates it to one or more hidden layers. The hidden layers perform all the processing and pass the outcome to the output layer.

Neural networks are typically used to derive meaning from complex and non-linear data, detect and extract patterns which cannot be noticed by the human brain. Here are some of the standard applications of neural network used these days.

These applications fall into different types of neural networks such as convolutional neural network, recurrent neural networks, and feed-forward neural networks. The first one is more used in image recognition as it uses a mathematical process known as convolution to analyze images in non-literal ways.

Let’s understand neural network in R with a dataset. The dataset consists of 724 observations and 7 variables.

The following codes runs the network classifying ‘Status’ as a function of several independent varaibles.