Neural Networks and use cases in Industries

What are Neural Networks?

A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon.

The most groundbreaking aspect of neural networks is that once trained, they learn on their own. In this way, they emulate human brains, which are made up of neurons, the fundamental building block of both human and neural network information transmission.

Artificial neural networks are built like the human brain, with neuron nodes interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards (inputs) and away (outputs) from the brain.

An ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report. Just like humans need rules and guidelines to come up with a result or output, ANNs also use a set of learning rules called backpropagation, an abbreviation for backward propagation of error, to perfect their output results.

𝐖𝐡𝐲 𝐃𝐨 𝐖𝐞 𝐔𝐬𝐞 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬?

Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications. Because neural networks also have the unique capacity (known as fuzzy logic) to make sense of ambiguous, contradictory, or incomplete data, they are able to use controlled processes when no exact models are available.

Applications of Neural Networks :

• Aerospace − Autopilot aircrafts, aircraft fault detection.
• Automotive − Automobile guidance systems.
• Military − Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image identification.
• Electronics − Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis.
• Financial − Real estate appraisal, loan advisor, mortgage screening, corporate bond rating, portfolio trading program, corporate financial analysis, currency value prediction, document readers, credit application evaluators.
• Speech − Speech recognition, speech classification, text to speech conversion.
• Telecommunications − Image and data compression, automated information services, real-time spoken language translation.

How ANN are currently being applied?

• Artificial Neural Networks are Improving Marketing Strategies
• Developing Targeted Marketing Campaigns
• Reducing Email Fatigue and Improving Conversion Rates
• Improving Search Engine Functionality
• Applications of neural networks in the pharmaceutical industry
• Developing Personalised Treatment Plans

Applications in Deep Learning and Artificial Intelligence :

• Artificial neural networks are a form of deep learning.
• They are also one of the main tools used in machine learning.
• Consequently ANN’s play an increasingly important role in the development of artificial intelligence.
• The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”.
• This technique allows the system’s hidden layers to become versatile.

Some Industry use cases of Neural Network

• Facebook:

Facebook achieved web dominance by riding a business model of understanding users and feeding them tailored content and advertising. And as the social networking company further builds on its strong position, it leans heavily on deep learning models.

- Understanding text with deep learning:

It's not all about images and videos, though. Facebook also uses natural language processing algorithms to interpret textual content and improve the quality of posts shown to users. Facebook uses an NLP system built around neural networks to identify posts that are excessively promotional, spam or clickbait. The deep learning model filters these types of posts out and keeps them from showing in users' news feeds. There's a huge amount of textual content that's being uploaded on Facebook every day, and understanding that is important to improving customer experience. Deep learning models are helping Facebook develop products by enabling developers to understand content at a large scale.

- Deep learning for computer vision:

For example, computer vision neural network deep learning models are used to interpret the content of photos users have posted and decide which to surface in the "on this day" feature. This Facebook feature shows users' posts that they made on the same day in past years.

So the models underlying the feature have to interpret images and develop a semantic understanding of what's happening to ensure it's something people would want to be reminded of. It does this in part by identifying people and objects in images and interpreting the context around them. The models were trained on more than a billion photos that have been uploaded to Facebook over the years, and they have to score in real time millions of new images uploaded each day. Tulloch said this is a huge technical challenge, but one for which the convolutional neural networks his team uses are well-suited.

• Siemens:

A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors. Often one of the output vectors is precomputed, thus forming a baseline against which the other output vector is compared. This is similar to comparing fingerprints but can be described more technically as a distance function for locality-sensitive hashing.

It is possible to build an architecture that is functionally similar to a siamese network but implements a slightly different function. This is typically used for comparing similar instances in different type sets.

Uses of similarity measures where a twin network might be used are such things as recognizing handwritten checks, automatic detection of faces in camera images, and matching queries with indexed documents. The perhaps most well-known application of twin networks are face recognition, where known images of people are precomputed and compared to an image from a turnstile or similar. It is not obvious at first, but there are two slightly different problems. One is recognizing a person among a large number of other persons, that is the facial recognition problem. DeepFace is an example of such a system. In its most extreme form this is recognizing a single person at a train station or airport. The other is face verification, that is to verify whether the photo in a pass is the same as the person claiming he or she is the same person. The twin network might be the same, but the implementation can be quite different.

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