What is a Neural Network?

A Neural network is best described as a set of algorithms with the capability to recognise the fundamental relationships between a collection of data via a technique similar to how the human brain analyses and processes information. Simply put, a neural network can identify hidden patterns and connections in raw data, cluster and classify it, and continue to learn with the aim of improving results.

The first neural network model was proposed and developed by Warren McCulloch and Walter Pitts in 1943

A Simple Neural Network

A Simple neural network consists of three layers, namely, an input layer, hidden layer and an output layer.

The input layer consists of x input features used to predict the output y whereas the hidden layer consists of an activation function such as ReLu (Rectified Linear Unit) to map x to y. In a sense, given adequate number of (x,y); training data, neural networks can remarkably figure out functions to map x to y.

Neural networks has a huge impact in improving decision processes in popular areas such as,

1. Natural language processing

2. Computer vision

3. Targeted Marketing

4. Disease and medical diagnosis

5. Financial predictions

Types of Neural networks

There are numerous types of neural networks each with strengths unique to the said neural network such as,

1. Feedforward Neural Network- Data moves in only one direction; forward, until the output node is reached where there is no backpropagation. These networks are used in face recognition and computer vision as the target classes in these applications are hard to classify.

2. Multilayer Perceptron: It is a fully connected neural network where every node is connected to the nodes in the following layer and uses a non linear activation function. These networks are used in speech recognition and machine translations.

Applications of Neural Networks

• Neural networks are used in Health and life sciences organisations to enable predictive diagnostics, biomedical imaging and health monitoring

• Energy and manufacturing companies use neural networks to defect detection automation, energy forecasting and supply chain optimization.

• The communications and retail industry uses neural networks to power conversational chatbots, customer intelligence enhancement, and in network analysis.

Neural networks can operate and process raw data in various ways to achieve the different outcomes. Unlike traditional machine learning, neural networks possess the capability to learn and grow with more data to train on. And so, it is believed that neural networks will be the fundamentals where next-generation Artificial Intelligence will drive on.