Deep Learning Applications You Can’t Afford to Ignore

Deep Learning Applications You Can’t Afford to Ignore


The last couple of years have witnessed a dramatic increase in the popularity of deep learning, an artificial intelligence approach inspired by the activities of a human brain. Till today, deep learning models have come up with innovative solutions in the areas of natural language processing, pattern recognition, computer vision, and many more.

Deep learning is a part of the machine learning family and it focuses on the algorithms inspired by the brain’s function and structure known as artificial neural networks. The idea of deep learning can be described as utilizing brain simulations to make learning algorithms much easier and better to use and make revolutionary advances in artificial intelligence and machine learning.

This technology trend has the potential to significantly impact the initiatives, programs, and long-term plans of an organization. Many organizations around the world are assessing how they can implement deep learning to gain competitive advantage. To get a better understanding of the deep learning’s potential, let’s dig deeper into some of its major applications.

Self-Driving Cars

Self-driving cars is one of the major applications of deep learning these days. Almost all the big players in the automobile industry are getting on the concept of self-driving cars. These systems utilize a neural network and sensors to process a massive amount of data. The self-driving car powered by deep learning technology can recognize obstacles like other cars, buildings, roads, trees, etc, and react accordingly. When machines are given enough data, they learn how to drive better than humans.


There are many applications of deep learning in healthcare but many are in the development phase. Few of the major healthcare applications include improving the accuracy of diagnosis, medicines tailored for specific genes, and many more. In general, we humans are prone to error. So, around 20 percent of the diagnoses turn out to be inaccurate. Deep learning enables much higher accuracy when given enough data.

Medicines affect people differently(while some people may be treated perfectly, others may experience nasty side-effects) and this is a known fact. With the help of deep learning process, the right medicine for a particular genetic makeup can be devised. Many companies are researching on this concept.


One of the major pain points of marketing professionals is there will be too many data elements to organize, segment, and learn from. Due to numerous analytics tools available today, marketing professionals are suffering from data overload. By employing deep learning algorithms, the marketing professionals can bring in a proper order to their marketing data and offer real-time recommendations for the targeting audience, content marketing, and campaign timing.


You may be swamped with a lot of emails every day from various people trying to sell you something or the other. Thanks to deep learning technology, your emails will be more targeted and personalized. This means they will occur in your inbox only during a particular time frame when you are most likely to click on them and provide a positive response. For example, Salesforce Einstein enables salespeople to use their time productively concentrating on the leads that are highly important via predictive lead scoring.


Things like scheduling of meetings are getting automated in large and medium enterprises with the help of deep learning technology. In a similar way, recruitment chatbots such as Mya save valuable resources and time for organizations in the talent acquisition process by handling communication with prospective talent and screening the candidates. Tools such as will join our conference calls and share a summarized outline with to-do lists and action-points to all the participants.

Final Thoughts

Though deep learning is being used extensively, it’s full potential isn’t explored yet. Many theoretical applications of deep learning are in the development phase today. When used productively, deep learning will become the most significant driver for the transformation of business functions in the future.

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