Neural Network And It’s Use-Case
Why To Use Neural Network!!
Over the past few years, technology has become very dynamic. It is fuelling itself at an ever increasing rate. Computers are a prime component of this whole revolution. Computers that can help fight diseases by designing new drugs, computers that can design better computers, computers that simulate reality and what not! This is a very exciting time for technology as the traditional boundaries are now becoming blurred.
We often tend to think that computers can only decide on whether a statement is true or false. Such logical statements are then linked together to form a series of rules. To program a ‘ computer, all that is needed is to precisely define the problem, write a specification and use these rules. The program tells the computer, rule by rule, exactly what to do. But it is difficult to program a computer for more ‘subbjective’ tasks, like predicting what the weaiher is going to be, or what the price of gold will be tomorrow.These tasks are in fact impossible to define accurately. Patterns need to be recognised that are complex and imperfect. Nature is chaotic and we need something to decode this chaos.
A different approach is needed to give computers more ‘human-like’ abilities, capability to make judgements, guesses and to change opinions. We humans learn by example and do not need to see every examples to make a guess, a judgement based upon what we have been taught.
What Is Neural Network?
Neural networks can be taught to perform complex tasks . They are massively parallel, extremely fast and intrinsically fault-tolerant. They learn from experience(models), generalise from examples(provided dataset), and are able to extract essential characteristics from noisy data. They require significantly less development time and can respond to situations unspecified or not previously envisaged. They are ideally suited to real-world applications and can provide solutions to a hos’ of currently impossible or commercially impractical problems.
In simple terms, a neural network is made up of a number of processing elements called neurons, whose interconnections are called synapses. Each neuron accepts inputs from either the external world or from the outputs of other neurons. Output signals from all neurons eventually propagate their effect across the entire network to the final layer where the results can be output to the real world. The synapses have a processing value or weight, which is learnt during training of the network. The functionality and power of the network primarily depends on the number of neurons in the network, the interconnectivity patterns or topology, and the value of the weights assigned to each synapse.
How do Neural Networks work?
A neural network is a bundle of neurons connected by synapses. Talking about the artificial one, the role of neurons are played by the units that perform calculations. Each of these “neurons”:
- receives data from the input layer;
- processes it performing simple calculations with it;
- and then transmits it to another “neuron”.
Usually, neural networks consist of three types of neurons:
- input;
- output;
- hidden.
Only single layer neural networks make an exception. They don’t have hidden neurons.
The synapses are responsible for connecting neurons with each other. Each neuron has got multiple outcoming synapses that attenuate or amplify the signal. This makes it possible for the neurons to work in the same way, but to show the different results depending on a certain situation.
Neural Network Application
e-Commerce
This technology is used in this industry for various purposes. But the most frequent example of artificial neural network application in eCommerce is personalizing the purchaser’s experience. For instance, Amazon, Flipkart, and other eCommerce platforms use AI to show the related and recommended products. The compilation is formed on the basis of the users’ behavior. The system analyzes the characteristics of certain items and shows similar ones. In other cases, it defines and remembers the person’s preferences and shows the items meeting them.
As for more complicated applications of neural networks in eCommerce, there is a very interesting startup called PixelDTGAIN. This product is developed to help sellers save the budget on photographers’ services. There is no need to organize photo sets as the special algorithm automatically makes the pictures of the clothes worn by models. All is needed to do is to resize the images of the items to 64*64, and get the result.
NEURAL NETWORKS- INDIAN SCENARIO
Lot of opportunities exist in the country for Al technologies, especially neural computing applications. Though most of the work is being done around robotics and expert systems, there are also people and organisations capable of developing neural system products. The potential sectors of application range from manufacturing, banking and finance, defence, telecommunications, pharmaceuticals to holiday industry.
Substantial amount of work is being done at the Centre for Artificial Intelligence and Robotics (CAIR, Bangalore) and the Institute for Robotics and Intelligent Systems (IRIS, Bangalore). They have developed a neural network for optical character recognition. The project is complete and awaits commercialisation. IRIS is working on functional electrical simulation using neural networks to simulate the muscles of a handicapped person and allow him to walk.
Scientists at the Indian Statistical lnstitute (Machine Intelligence Unit), Calcutta, have figured out computer simulated models, more advanced than human brain, for creating artificial entities more intelligent than present day systems in performing cognitive tasks. This project will have far reaching implications on medical research and robotics.
Conclusion
Neural computers perform very favourably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise or incomplete data. Furthermore, knowledge is encapsulated in a compact, efficient way that can easily be adapted to changes in business environment.
As with all technologies, there is a window of opportunity for exploitation-and that window is here today. You cannot afford to ignore the fact that your competitors are already investigating the opportunities and realising the significant business benefits that neural technology brings to a range of applications.
The reason one should use neural computing technology is the competition!