Applications of Deep Learning to Real-World Analytics Scenarios
Date: April 4, 2017
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What Is Deep Learning?

Many people talk about Deep Learning these days but few actually know what they are talking about. Fortunately there is a plethora of articles and books available on the topic. Yet, you don’t need to go anywhere else to find out more about this fascinating part of data science since Data Science Partnership is one of the places where this technology flourishes. In essence, Deep Learning is a series of machine learning methods that employ Artificial Intelligence (AI) in the form of sophisticated Artificial Neural Networks (ANNs), to tackle complex problems. There are a couple of catches though. First of all, in order to do something useful with Deep Learning you need to have a lot of data. Also, in order to make the most of this data using Deep Learning, you need to have people who know the ins and outs of Deep Learning, since it is a quite complicated process to get a large ANN to do its magic.

Why Is Deep Learning Relevant?

Unlike many other technologies that seem to appear whimsically in the data science field without taking roots, Deep Learning is here to stay as it provide substantial benefits to the organizations that utilize this technology. The reason is simple. Deep Learning manages to accelerate the whole process of insight discovery known as the data science pipeline. That’s not to say that it fully automates the whole process though. Contrary to what many AI evangelists claim, all AI technologies are heavily dependent on specialized experts who have a solid grasp of data science and AI, as well as a decent business acumen. Machines have grown more robust in the past years but the idea of them becoming autonomous is still in the realm of science fiction.

Moreover, Deep Learning systems outperform conventional machine learning systems, as they manage to obtain a better generalization in whatever problem they are tackling. Part of the reason why is that they employ an entirely data-driven approach, making them unbiased and versatile (something inconceivable for statistical data analysis systems). The improvement in performance is also quite noticeable, making Deep Learning a viable option for any data analytics project

How Does Deep Learning Apply in the Real World?

So, how does all this benefit you and your organization? Well, it can offer you the means to obtain better performance in your data science projects if you are involved in one or more of the following domains:

  • Data mining
  • Signal processing and analysis
  • Image recognition
  • Sound analytics
  • Other domains of high complexity

Naturally, the industries that have the most to gain from this technology are:

  • Finance
  • Retail
  • Telecommunications & IT
  • Aerospace & Defense
  • Media and Advertising
  • Medical
  • Automotive
  • Industrial
  • Oil, Gas, and Energy

Also, although the majority of organizations employing Deep Learning are in North America, there is a lot of interest in other parts of the world, particularly Europe (mainly U.K., France, and Germany), Eastern Asia (mainly China, South Korea, India, and Japan), and other parts of the globe (e.g. Middle East and Latin America).

The expected worth of the market Deep Learning taps into in the next 5 years is around 1.7 Billion USD, which is significantly greater than what it is today. Therefore, Deep Learning is much more than a fad, while its level of adoption in the industry and its integration in the data science field are only going to grow from now on.

Next Steps?

There are several options to harness the power of Deep Learning for your data analytics projects. For starters, you can get in touch with Data Science Partnership to either hire an expert in this field, or if you have a team in place already and wish to merely upgrade its know-how, learn how you can apply Deep Learning to the data available to you through a few training sessions. The latter can be in your venue or online, depending on your requirements and schedule. Whatever you decide to do, we are happy to facilitate you in making the most of this promising and robust technology.

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Zacharias Voulgaris

Zach is the Chief Technical Officer at Data Science Partnership. He studied Production Engineering and Management at the Technical University of Crete, shifted to Computer Science through a Masters in Information Systems & Technology (City University of London), and then to Data Science through a PhD on Machine Learning (University of London). He has worked at Georgia Tech as a Research Fellow, at an e-marketing startup in Cyprus as an SEO manager, and as a Data Scientist in both Elavon (GA) and G2 (WA). He also was a Program Manager at Microsoft, on a data analytics pipeline for Bing.


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