Research for industry usecases of Neural Networks :-

* What are Neural Networks?

Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.

It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation

First developed in the 1940s Artificial Neural Networks attempt to simulate the way the brain operates.

Sometimes called perceptrons, an Artificial Neural Network is a hardware or software system.

Some networks are a combination of the two.

Consisting of a network of layers this system is patterned to replicate the way the neurons in the brain operate.

The network comprises an input layer, where data is entered, and an output layer.

The output layer is where processed information is presented.

Connecting the two is a hidden layer or layers.

The hidden layers consist of units that transform input data into useful information for the output layer to present.

In addition to replicating the human decision making progress Artificial Neural Networks allow computers to learn.

Their structure also allows ANN’s to reliably and quickly identify patterns that are too complex for humans to identify.

Artificial Neural Networks also allow us to classify and cluster large amounts of data quickly.

* Real-Life Application Of Artificial Neural Networks

In the foreseeable future, ANNs may play a key part in helping build human-level artificial intelligence. Currently, though, neural networks are finding application in a number of industries, from healthcare to retail. In more concrete terms, which consumer and business apps were built using ANNs? Could we be using them now, not knowing we are experiencing a neural network in action? For example, social networks like LikedIn, use neural network tools to filter out abusive content, to recognize content types and provide recommendations. Kairos is a company using Machine Learning and face recognition techniques to create advanced tools for fraud detection. Virtual assistants like Apple’s Siri or Google Assistant are familiar to everyone. Cortana and Speechnotes are other examples of business software based on neural networks. Listenbycode is another ANN-based system for converting voice into text. At VARTEQ we have robust experience of using artificial neural networks to build a variety of tools and services. For example, we have used convolutional neural networks to develop a price label recognition systems for the Client (under NDA). We have also built a service used for search and detection of ads and products on photographs and videos using a Convolutional neural network (CNN) and Computer Vision. The service helps marketers evaluate their brand products visibility and screen time on videos and improve targeting of their marketing campaigns. It is also used in merchandising — to see how many of the brand products are placed on the front shelves in the retail stores. We have also used technologies like Convolutional neural network (CNN), Recursive neural network (RNN), Long short-term memory (LSTM), and Sequence-to-sequence models to build a range of solutions for healthcare sector to assist radiologists, ultrasound diagnostics and other medical scanning experts in processing medical images

* How Facebook uses Deep Learning for Chatbot army

Although Facebook’s Messenger service is still a little…contentious (people have very strong feelings about messaging apps, it seems), it’s one of the most exciting aspects of the world’s largest social media platform. That’s because Messenger has become something of an experimental testing laboratory for chatbots

Any developer can create and submit a chatbot for inclusion in Facebook Messenger. This means that companies with a strong emphasis on customer service and retention can leverage chatbots, even if they’re a tiny startup with limited engineering resources.

Of course, that’s not the only application of machine learning that Facebook is interested in. AI applications are being used at Facebook to filter out spam and poor-quality content, and the company is also researching computer vision algorithms that can “read” images to visually impaired people