Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow iconNeural Networks: Applications in the Real World

Neural Networks: Applications in the Real World

Last updated:
6th Feb, 2018
Views
Read Time
8 Mins
share image icon
In this article
Chevron in toc
View All
Neural Networks: Applications in the Real World

Neural Networks find extensive applications in areas where traditional computers don’t fare too well. Like, for problem statements where instead of programmed outputs, you’d like the system to learn, adapt, and change the results in sync with the data you’re throwing at it. Neural networks also find rigorous applications whenever we talk about dealing with noisy or incomplete data. And honestly, most of the data present out there is indeed noisy.

Best Machine Learning and AI Courses Online

With their brain-like ability to learn and adapt, Neural Networks form the entire basis and have applications in Artificial Intelligence, and consequently, Machine Learning algorithms. Before we get to how Neural Networks power Artificial Intelligence, let’s first talk a bit about what exactly is Artificial Intelligence.

For the longest time possible, the word “intelligence” was just associated with the human brain. But then, something happened! Scientists found a way of training computers by following the methodology our brain uses. Thus came Artificial Intelligence, which can essentially be defined as intelligence originating from machines. To put it even more simply, Machine Learning is simply providing machines with the ability to “think”, “learn”, and “adapt”.

Ads of upGrad blog

In-demand Machine Learning Skills

With so much said and done, it’s imperative to understand what exactly are the use cases of AI, and how Neural Networks help the cause. Let’s dive into the applications of Neural Networks across various domains – from Social Media and Online Shopping, to Personal Finance, and finally, to the smart assistant on your phone.

You should remember that this list is in no way exhaustive, as the applications of neural networks are widespread. Basically, anything that makes the machines learn is deploying one or the other type of neural network.

Social Media

The ever-increasing data deluge surrounding social media gives the creators of these platforms the unique opportunity to dabble with the unlimited data they have. No wonder you get to see a new feature every fortnight. It’s only fair to say that all of this would’ve been like a distant dream without Neural Networks to save the day.

FYI: Free Deep Learning Course!

Neural Networks and their learning algorithms find extensive applications in the world of social media. Let’s see how:

Facebook

As soon as you upload any photo to Facebook, the service automatically highlights faces and prompts friends to tag. How does it instantly identify which of your friends is in the photo?
The answer is simple – Artificial Intelligence. In a video highlighting Facebook’s Artificial Intelligence research, they discuss the applications of Neural Networks to power their facial recognition software. Facebook is investing heavily in this area, not only within the organization, but also through the acquisitions of facial-recognition startups like Face.com (acquired in 2012 for a rumored $60M), Masquerade (acquired in 2016 for an undisclosed sum), and Faciometrics (acquired in 2016 for an undisclosed sum).
Neural Networks: Applications in the Real World UpGrad Blog
In June 2016, Facebook announced a new Artificial Intelligence initiative that uses various deep neural networks such as DeepText – an artificial intelligence engine that can understand the textual content of thousands of posts per second, with near-human accuracy.

Instagram

Instagram, acquired by Facebook back in 2012, uses deep learning by making use of a connection of recurrent neural networks to identify the contextual meaning of an emoji – which has been steadily replacing slangs (for instance, a laughing emoji could replace “rofl”).
By algorithmically identifying the sentiments behind emojis, Instagram creates and auto-suggests emojis and emoji related hashtags. This may seem like a minor application of AI, but being able to interpret and analyze this emoji-to-text translation at a larger scale sets the basis for further analysis on how people use Instagram.

Pinterest

Pinterest uses computer vision – another application of neural networks, where we teach computers to “see” like a human, in order to automatically identify objects in images (or “pins”, as they call it) and then recommend visually similar pins. Other applications of neural networks at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.

Online Shopping

Do you find yourself in situations where you’re set to buy something, but you end up buying a lot more than planned, thanks to some super-awesome recommendations?
Yeah, blame neural networks for that. By making use of neural network and its learnings, the e-commerce giants are creating Artificial Intelligence systems that know you better than yourself. Let’s see how:

Search

Your Amazon searches (“earphones”, “pizza stone”, “laptop charger”, etc) return a list of the most relevant products related to your search, without wasting much time. In a description of its product search technology, Amazon states that its algorithms learn automatically to combine multiple relevant features. It uses past patterns and adapts to what is important for the customer in question.
And what makes the algorithms “learn”? You guessed it right – Neural Networks!

Recommendations

Amazon shows you recommendations using its “customers who viewed this item also viewed”,  “customers who bought this item also bought”, and also via curated recommendations on your homepage, on the bottom of the item pages, and through emails. Amazon makes use of Artificial Neural Networks to train its algorithms to learn the pattern and behaviour of its users. This, in turn, helps Amazon provide even better and customized recommendations.

Banking/Personal Finance

Cheque Deposits Through Mobile

Most large banks are eliminating the need for customers to physically deliver a cheque to the bank by offering the ability to deposit cheques through a smartphone application. The technologies that power these applications use Neural Networks to decipher and convert handwriting on checks into text. Essentially, Neural Networks find themselves at the core of any application that requires handwriting/speech/image recognition.

Fraud Prevention

How can a financial institution determine a fraudulent transaction? Most of the times, the daily transaction volume is too much to be reviewed manually. To help with this, Artificial Intelligence is used to create systems that learn through training what types of transactions are fraudulent (speak learning, speak Neural Networks!).

FICO – the company that creates credit ratings that are used to determine creditworthiness, makes use of neural networks to power their Artificial Intelligence to predict fraudulent transactions. Factors that affect the artificial neural network’s final output include the frequency and size of the transaction and the kind of retailer involved.

Powering Your Mobile Phones

Voice-to-Text

One of the more common features on smartphones today is voice-to-text conversion. Simply pressing a button or saying a particular phrase (“Ok Google”, for example), lets you start speaking to your phone and your phone converts the audio into text. Google makes use of artificial neural networks in recurrent connection to power voice search. Microsoft also claims to have developed a speech-recognition system – using Neural Networks, that can transcribe conversations slightly more accurately than humans.

Smart Personal Assistants

With the voice-to-text technology becoming accurate enough to rely on for basic conversations, it is turning into the control interface for a new generation of personal assistants. Initially, there were simpler phone assistants – Siri and Google Now (now succeeded by the more sophisticated Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar. Amazon expanded upon this model with the announcement of complementary hardware and software components – Alexa, and Echo (later, Dot).

Popular AI and ML Blogs & Free Courses

A Beginner's Guide To Natural Language Understanding UpGrad Blog

Ads of upGrad blog

To Wrap Up…
We’ve only scratched the surface when it comes to the applications of neural networks in day-to-day life. Specific industries and domains have specific interactions with Artificial Intelligence by making use of neural networks which is far beyond what’s talked about in this article. For example, chess players regularly use chess engines to analyze their games, improve themselves, and practice new tactics – and it goes without saying that the chess engine in question deploys Neural Networks to accomplish the learning.

Learn ML courses Online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

Do you have any other interesting real-life use case of Neural Networks that we might have missed? Drop it in the comments below!

Profile

Reetesh Chandra

Blog Author
Reetesh is Project Manager of Data Sciences Program at UpGrad. He manages end-to-end student experience of the Data Sciences program.
Get Free Consultation

Select Coursecaret down icon
Selectcaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Frequently Asked Questions (FAQs)

1What problems can neural networks solve?

Neural networks solve problems that require pattern recognition. For example, a neural network could be trained to recognize handwritten digits. Another example is the Google self-driving car, which is trained to classically recognize a dog, a truck, or a car. They are good for Pattern Recognition, Classification and Optimization. This includes handwriting recognition, face recognition, speech recognition, text translation, credit card fraud detection, medical diagnosis and solutions for huge amounts of data. It can be used to find links between patterns, to convert one type of data to another and to make associations or generalizations between different entities.

2Why are neural networks important?

Neural networks are a class of machine learning algorithms that have many applications. Some of the most popular applications of neural networks are computer vision, speech recognition, and natural language processing. Today, neural networks are being used for a wide range of applications and are enjoying a lot of attention from the research community. ANNs can be used to address many difficult problems that are faced today. They are employed as a component in a larger system, or can be used in the pre-processing stage of complicated non-linear techniques.

3What is the biggest problem with neural networks?

The biggest problem with neural networks is that they are not that accurate, mostly because they have a relatively slow learning curve. And the problem isn't just with accuracy, but also with efficiency. Neural networks can be extremely slow to operate, because many times they rely on feedback from previous computations to the next one. A simple way to solve this will be taking out one of the many layers of the network to avoid such feedback, but this might actually damage the accuracy of the network. Another solution could be to use parallel computers, which can be used to divide the workload and eliminate problems of speed.

Explore Free Courses

Suggested Blogs

Artificial Intelligence course fees
5060
Artificial intelligence (AI) was one of the most used words in 2023, which emphasizes how important and widespread this technology has become. If you
Read More

by venkatesh Rajanala

29 Feb 2024

Artificial Intelligence in Banking 2024: Examples & Challenges
5442
Introduction Millennials and their changing preferences have led to a wide-scale disruption of daily processes in many industries and a simultaneous g
Read More

by Pavan Vadapalli

27 Feb 2024

Top 9 Python Libraries for Machine Learning in 2024
75055
Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learn
Read More

by upGrad

19 Feb 2024

Top 15 IoT Interview Questions & Answers 2024 – For Beginners & Experienced
64134
These days, the minute you indulge in any technology-oriented discussion, interview questions on cloud computing come up in some form or the other. Th
Read More

by Kechit Goyal

19 Feb 2024

Data Preprocessing in Machine Learning: 7 Easy Steps To Follow
149952
Summary: In this article, you will learn about data preprocessing in Machine Learning: 7 easy steps to follow. Acquire the dataset Import all the cr
Read More

by Kechit Goyal

18 Feb 2024

Artificial Intelligence Salary in India [For Beginners & Experienced] in 2024
907572
Artificial Intelligence (AI) has been one of the hottest buzzwords in the tech sphere for quite some time now. As Data Science is advancing, both AI a
Read More

by upGrad

18 Feb 2024

24 Exciting IoT Project Ideas & Topics For Beginners 2024 [Latest]
752423
Summary: In this article, you will learn the 24 Exciting IoT Project Ideas & Topics. Take a glimpse at the project ideas listed below. Smart Agr
Read More

by Kechit Goyal

18 Feb 2024

Natural Language Processing (NLP) Projects & Topics For Beginners [2023]
106453
What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computation
Read More

by Pavan Vadapalli

17 Feb 2024

45+ Interesting Machine Learning Project Ideas For Beginners [2024]
325973
Summary: In this Article, you will learn Stock Prices Predictor Sports Predictor Develop A Sentiment Analyzer Enhance Healthcare Prepare ML Algorith
Read More

by Jaideep Khare

16 Feb 2024

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon