Artificial Neural Network

In information technology (IT),  Artificial Neural Network (ANN) is a computational learning system. It is also known as Neural network or just Neural Net. It  uses a network of functions to understand and translate a data input of one form into a desired output. It is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

The history of artificial neural networks goes back to the early days of computing. In 1943, mathematicians Warren McCulloch and Walter Pitts built a circuitry system intended to approximate the functioning of the human brain that ran simple algorithms. It wasn't until around 2010 that research picked up again. The big data trend, where companies amass vast troves of data, and parallel computing gave data scientists the training data and computing resources needed to run complex artificial neural networks. In 2012, a neural network was able to beat human performance at an image recognition task as part of the ImageNet competition. Since then, interest in artificial neural networks as has soared and the technology continues to improve.

Now let’s try to understand how neural network works. Machine learning algorithms that use neural networks generally do not need to be programmed with specific rules that define what to expect from the input. The more examples and variety of inputs the program sees, the more accurate the results typically become because the program learns with experience. 

 An ANN usually involves a large number of processors operating in parallel and arranged in tiers. The first tier receives the raw input information -- analogous to optic nerves in human visual processing. Each successive tier receives the output from the tier preceding it, rather than the raw input -- in the same way neurons further from the optic nerve receive signals from those closer to it. The last tier produces the output of the system. Typically, an ANN is initially trained or fed large amounts of data. Training consists of providing input and telling the network what the output should be. For example, to build a network that identifies the faces of actors, the initial training might be a series of pictures, including actors, non-actors, masks, statuary and animal faces. Each input is accompanied by the matching identification, such as actors' names or "not actor" or "not human" information. Providing the answers allows the model to adjust its internal weightings to learn how to do its job better.

Neural networks can be applied to a broad range of problems and can assess many different types of input, including images, videos, files, databases, and more. They also do not require explicit programming to interpret the content of those inputs.

Because of the generalized approach to problem solving that neural networks offer, there is virtually no limit to the areas that this technique can be applied. Some common applications of neural networks today, include image/pattern recognition, self driving vehicle trajectory prediction, facial recognition, data mining, email spam filtering, medical diagnosis, and cancer research. There are many more ways that neural nets are used today, and adoption is increasing rapidly.