Introduction to Neural Networks and their use?

Introduction to Neural Networks and their use?

An Artificial Neural Network is an information processing model that is inspired by the way our brain process information. The basic idea behind a neural network is to copy in a simplified but reasonably faithful way lots of densely interconnected brain cells inside a computer so you can get it to learn things, make decisions, and recognize patterns in a human like way. One of the most wonderful thing neural networks is that it does not need to be programmed, it learns all by itself through examples, just like the brain.

Neural networks use a very different approach for solving problems from the one used by conventional computers. The conventional computers use the algorithmic approach, which means the computer follows the set of instructions in order to solve a problem. The computer needs to be fed with particular set of instructions to solve a problem, without the instructions it cannot solve the problem.Where on the other hand, neural networks process information in the same way as our brain does. The network is composed of highly interconnected processing elements called neurons, which work in parallel to solve a particular problem. In short we can say that neural networks learn from examples. They need not be programmed to perform a specific task. The examples must be carefully selected otherwise useful time is wasted and also the network might not work correctly.

A typical neural network has artificial neurons called units which vary in range from few dozen to hundreds, thousands, or even millions and are arranged in a series of layers, each of which connects to the layer on either side. These units have been divided into three categories, which are: Input Units, designed to receive various forms of information from outside world that the network will learn about, recognize, or otherwise process.

Other units sit on the opposite side of the network and signal how it responds to the information it learned, these are known as Output Units. In between the output and input units there lie one or more layers of Hidden Units, which together form the majority of the Artificial Brain. Most of the neural networks are fully connected, which means each hidden unit and each output unit is connected to every unit in the layers either side.

Neural networks, have a remarkable ability to derive meaning from complex or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be regarded as an expert in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer “what if” questions. Neural Networks can also be used in the following fields:

Adaptive Learning: This includes the ability to learn on how to perform the task based on the data provided for training or initial experience. Like, Object detection and recognition application require large amount of data to train the model and perform the testing to validate about the object.

Self-Organization: An artificial neural network can create its own organization or representation of the information it receives during learning. Now days, multiple AI Companies are in the beginning stage to provide AI Services and Products.

Real Time Operation:Artificial Neural Networks computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability. There are various Real-time applications such as Stock Market Prediction System, Sports/Gaming Betting System,  E-Commerce Recommendation System.

Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

Conclusion: The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture. Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain. Therefore, neural networks do not perform miracles. But if used sensibly they can produce some amazing results.

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