NEURO NETWORK

Technology and the brain are very closely related in these days. Modern computer applications take into account the features of human brains (in marketing, for example), and human brains take into account the features of technologies (if you need the direction… no worries, there’s Google Maps).

To give machines 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 . The human Sensing system is one of the wonders of the world. Consider the following sequence of handwritten digits:

13579

Most people effortlessly recognize those digits as 13579. That ease is deceptive. In each hemisphere of our brain, humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. And yet human vision involves not just V1, but an entire series of visual cortices - V2, V3, V4, and V5 - doing progressively more complex image processing. We carry in our heads a supercomputer, tuned by evolution over hundreds of millions of years, and superbly adapted to understand the world. Recognizing handwritten digits isn't easy. Rather, we humans are stupendously, astoundingly good at making sense of what our eyes show us. But nearly all that work is done unconsciously. And so we don't usually appreciate how tough a problem our visual systems solve.

If you Start to think how we first learn to recognize 9 - it has a loop at the top, and a vertical stroke in the bottom right . Similarly we learn how 1(one) , 2 (two) , other numbers look. We feed our brain with lots of data ,we learned numbers , then we start learn sequence of numbers , pattern of numbers (1, 3, 5, 7, 9 - odd number) , and now we can predict what is written as we trained our brain with lot of data of number . All this process of training , learning is done by the millions neuron network layer present in out brain .

Defining Neuro Network

Basically, a neuron is just a node with many inputs and one output. A neural network consists of many interconnected neurons. In fact, it is a “simple” device that receives data at the input and provides a response. First, the neural network learns to correlate incoming and outcoming signals with each other — this is called learning. And then the neural network begins to work — it receives input data, generating output signals based on the accumulated knowledge.

Every thing in machine is Program and Process , therefore if we define Neuro Network in terms of Program - Algorithm then A neural network 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. Neural networks can adapt to changing input , so the network generates the best possible result without needing to redesign the output criteria.

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.

Neurons are capable of changing their characteristics over a period of time . So, a typical neural network works like this

• it receives certain data through the input layer of neurons;
• the data is processed by the neurons and passed to the next layer with the help of synapses each of which has its own coefficient;
• the next layer of neurons receive the information that is the sum of all of all data for neural networks, which are multiplied by the weight coefficients (each by its own);
• the resulting value is substituted into the activation function, resulting in the formation of output information;

How AI , MI , DL & Neural networks relate?

The easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.

That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm .

Deep Learning(DL) and Neuro Network

The “deep” in deep learning is referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm . A neural network that only has two or three layers is just a basic neural network.

Most deep neural networks are feed-forward, meaning they flow in one direction only from input to output. However, you can also train your model through backpropagation; that is, move in opposite direction from output to input. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the algorithm appropriately.

Application / Use Cases of Neuro Network -

Neural networks are widely used in different industries , neural networks have become an accepted information analysis technology in a variety of disciplines. This has resulted in a variety of commercial applications (in both products and services) of neural network technology . Big companies and startups both use this technology. Most often, neural networks can be found in all kinds of industries: from eCommerce to vehicle building.

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.

 Companies like Amazon , AliExpress, 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 PixelDTGAN - 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.

Finance

In this industry, there are neural network applications for fraud detection, management, and forecasting.

A great example of neural network finance applications is SAS Real Time Decision Manager . It helps banks to find solution for business issue (for instance, whether to give credit to a certain person) analyzing risks and probable profits.

As for financial forecasting, there are plenty of solutions that predict the exchange rate changes. For example, the startup Finprophet is the software that uses a neural network of deep learning for giving the forecast about a wide range of financial instruments like currencies, cryptocurrencies, stocks, futures.

Healthcare

It is very difficult to create and train a neural network for usage in this industry because it requires high accuracy. For many years it seemed to be a fantasy to use this technology for examining patients and diagnosing them. But finally, it has become possible.

IBM Watson  is the most powerful artificial intelligence in the world. It took 2 years to train the neural network for medical practice. Millions of pages of medical academic journals, medical records, and other documents were uploaded to the system for its learning. And now it can prompt the diagnosis and propose the best treatment pattern based on the patient’s complaints and anamnesis.

Security

Neural networks are widely used for protection from computer viruses, fraud, etc.

One of the examples is ICSP Neural from Symantec. It protects from cyber attacks by determining the bad USB devices containing viruses and exploiting zero-day vulnerabilities.

One more sample of using AI and ML for security purposes is Shape Security which provides several finance solutions.

 Logistics

This industry needs a lot of management that is to be done manually by employees of many companies. But nowadays, neural networks are capable of routing and dispatching.

For example, Wise Systems is an autonomous system which lets a user:

• plan routes and monitor them;
• customize shipping routes in real-time with the help of predictive features.

One more solution is Four Kites. This is a visibility program that works in a real-time mode. It helps to plan and monitor routes and predict the time of delivery.

Vehicle building

AI and ML are used in this industry to automate processes. For example, Tesla  uses a neural network for the autopilot system in the vehicles. With the help of trained artificial intelligence, it recognizes the road markings, detects obstacles, and makes the road safer for the driver.

Some more Fields where Neuro networks is playing essential roles are -

• Business -Marketing , Real Estate
• Document & Form Processing - Machine printed character recognition , Graphics recognition , Hand printed character recognition ,Cursive handwritten character recognition

• Finance Industry - Market trading , Fraud detection , Credit rating
• Food industry - Odor/aroma analysis , Product development ,Quality assurance
• Energy Industry - Electrical load forecasting , Hydroelectric dam operation , Natural gas
• Manufacturing Process control - Quality control
• Medical & Health Care Industry - Image analysis Drug development , Resource allocation
• Transportation & Communication