Case Study: Industry use cases of Neural network

Published by Anubhav Singh on

neural network

A neural network is a series of an algorithm intended to recognize the pattern and interpret data through clustering and labeling. In this article, we are going to discuss what is neural networks and their industry use cases.

What is Neural Network?

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. 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. The concept of neural networks, which has its roots in Artificial Intelligence(AI).

Neural networks are used to find the relationship between a large amount of data. It can be used in various AI works like forecasting and marketing research to fraud detection.

How do Artificial Neural Networks work?


As we know Artificial Neural network is made up of a number of different layers. These artificial neurons allow the layers to process, categorize and sort information.

Alongside the layers are processing nodes. Each node has its own specific piece of knowledge. This knowledge includes the rules that the system was originally programmed with. It also includes any rules the system has learned for itself.

This makeup allows the network to learn and react to both structured and unstructured information and data sets. Almost all artificial neural networks are fully connected throughout these layers. Each connection is weighted. The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit.

The first layer is the input layer. The hidden layer process and analyze the data. Eventually, it reaches the end of the network, the output network.

Educating Neural Network

For the artificial neural network to learn about any pattern there needs a lot of information. It learns about a data items by processing that large amount of data. ANN recognise the item and when in future it founds similar object with their model it reply the answer.

Use of Neural Network

Neural network can be applied to real world problems in many ways. In fact, they have already been implemented in many industries.

Let’s look at some examples of neural network applications in different area:

  • eCommerce –  for personalising the purchaser’s experience.
  • Finance – for fraud detection management and forecasting.
  • Healthcare – to examine patients and diagnosing.
  • Security – to avoid the computer viruses.

Case Study: Atomwise

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Atomwise uses Neural Networks to help discover new medicines and agricultural compounds. Its groundbreaking AtomNet technology reasons like a human chemist, using powerful deep learning algorithms and supercomputers to analyze millions of data about diseases.

Taken from company’s website “Atomwise is revolutionizing how drugs are discovered with AI. We invented the use of deep learning for structure-based drug discovery, today developing a pipeline of small-molecule drug candidates advancing into preclinical studies. Our AtomNet® technology has been used to unlock more undruggable targets than any other AI drug discovery platform. We are tackling over 600 unique disease targets across 775 collaborations spanning more than 250 partners around the world. To date, Atomwise has raised over $174 million from leading venture capital firms to advance our mission to make better medicines, faster.”

How does AtomNet works ?

AtomNet technology is the first drug discovery algorithm to use a deep convolutional neural network. This type of network came to prominence only a few years ago and has a unique property: it excels at understanding complex concepts as a combination of smaller and smaller pieces of information.

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This is what AtomNet platform does : when different neurons on the network are examined we see something new: AtomNet platform has learned to recognize essential chemical groups like hydrogen bonding, aromaticity, and single-bonded carbons. The patterns it independently observed are so foundational that medicinal chemists often think about them, and they are studied in academic courses. Put simply, AtomNet technology is teaching itself college chemistry.

So AI and neural networks can be applied to a vast number of use cases.

Thanks for reading this.

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