Top Real Life Deep Learning Applications Across Industries

The world is slowly turning into a holistically connected sphere. With the power of the internet and connected apps, the limitations that existed earlier are slowly being reconfigured – and a big part of this reason is Deep Learning. It is responsible for many changes in the world today, a majority of which have far-reaching implications on the way we live in the world.

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If you don’t fully understand what Deep Learning can achieve, just think of it this way – it can allow you to classify all your old images on any parameter you may decide (people, dates, locations, etc). Such is its potential, and the way it is being used today suggests precisely this direction for it in the future. 

Top Real Life Deep Learning Applications:

Fraud News Detection and News Aggregation

Deep Learning has facilitated greater levels of personalisation for newsreaders, and this isn’t even its most advanced rendition. With increased sophistication, it can now define reader personas and also account for filters based on geography, as well as social and economic factors.

Moreover, it can help with another related process, which is much more in demand today – fraud news detection. Given that the internet is relied on for almost all the information that users get today, it has become a task of prime importance to address the removal of fake news.

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Bots automatically replicate news from a singular source, so much so that it gets tough to tell which piece of news is original and which is fake – because both items are so prominently spread across the internet.

With the help of Deep Learning, certain classifiers can be developed which detect news that isn’t thoroughly objective and remove it from the feed you’re browsing. It can also aid in alerting users about potential privacy breaches.

Natural Language Processing (NLP)

It has often been said that a computer (or piece of code) can only understand based on a particular syntax and nothing beyond it. Well, NLP aims to change this assumption by learning how language is processed in real time, akin to how humans are already doing it.

Although it is still to reach a fully mature expression in this regard, the strides it has already made are significant enough, since it can help with multiple tasks across several verticals. This includes document summarization, which is employed so vastly in the legal field that it is threatening to replace all paralegals. Here, the training is similar to the way in which a human being learns to understand language.

From an early age, humans are constantly exposed to language and this impacts them in an inexplicable fashion. With time, they get used to certain words, as well as how they inform and disallow certain forms of expression. The idea behind NLP is that it needs to be trained constantly – through language modelling, classifying text, twitter analysis, answering questions, and sentiment analysis. These are all subsets of NLP and are gaining immense popularity.

Multiple Use Cases in the Entertainment Industry

Deep Learning doesn’t just have a significant impact in the legal or the information industry, it also plays a very prominent role in the entertainment industry, keeping millions of people on the hook for another minute (or an hour) every day. And some of these applications are truly surprising.

While it might seem intuitive now to think that Netflix provides a personalised user experience to its customers through the use of Deep Learning (and so does Amazon), it might be less obvious to think that VEVO has created a new generation of data services catering not just to users but also artists, record labels, companies, as well as internal business groups.

What else is Deep Learning enabling in the entertainment space? For starts, content editing as well as automatic content creation are now a reality, owing to the extent to which facial as well as pattern recognition have developed. Moreover, Deep Learning has changed the filmmaking process by making sure that cameras can understand human body language very well, making it easier to imbibe that into virtual characters.

In addition to this, Wimbledon 2018 extensively used IBM Watson to auto-generate highlights for telecast. It was possible to do this because IBM Watson could analyse the expressions as well as emotions of the players, and thus, they were able to save a fortune on both effort as well as cost.

Healthcare Industry

Perhaps another unintuitive use of Deep Learning, it has far-reaching and multiple implications when it comes to the healthcare industry. The entire healthcare industry, like all industries that have been impacted by emerging technologies, is going through a transformation – and GPU computing is driving a lot of it forward.

GPU-accelerated systems and applications are creating new possibilities in healthcare, helping in early detection and curing of life-threatening. There are also augmented clinicians who can virtually step in when physically there is a shortage. Various Deep Learning projects in this vein are picking up momentum in the industry, and they show promising signs of growth.

A prominent area in this regard is readmissions, which costs healthcare providers millions of dollars in revenue. By making use of deep learning as well as neural networks, healthcare providers and are reducing both the associated health risks as well as the costs. 

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Conclusion

Deep Learning is an up and coming phenomenon, and it is likely to be at the forefront of immense technological changes in the years to come. upGrad, in fact, provides a program in Deep Learning and Machine Learning – among other things, it teaches you to analyse X-ray images, predict customer churn across telecom providers, recognise gestures, and lots more!

Read more about the course, which is designed specifically for working individuals, here. If you want to be at the heart of technology in the next decade, Deep Learning is definitely the way to go for you!