What is Deep Learning and why is it so popular?

05 Nov 2018

deep learning
Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning is a technique that teaches computers to perform tasks as a human would. Machines can learn by experience and acquire skills without human intervention: learn by example. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance.

You can find deep learning behind driverless cars, face and voice recognition, virtual assistants, medical devices and others.
 
So why is Deep Learning so popular?

Deep Learning has become the main driver behind so many applications and its popularity comes from the fact that it's easy and it works. With its help we can achieve higher accuracy at a practical speed and it's more accessible in terms of the learning curve. Deep learning has significantly improved the state-of-the-art for many problems that machine learning and AI community faced for a lot of years. Deep Learning is already used in many industries from automated driving to medical devices.
 
Practical examples of Deep learning
 
Self-driving vehicles

Self-driving vehicles use a combination of technologies, sensors, cameras, radar and artificial intelligence to travel safely between destinations without human assistance. The field of autonomous driving is continuously developing, and the research demonstrates that self-driving vehicles will dramatically improve traffic flow as well as safety. Some deep-learning models specialize in streets signs while others are trained to recognize pedestrians.

Computer vision

Computer vision is a part of AI that helps computers analyze the content of images and video and distinguish different objects in the physical world to better understand what's going on around them. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems. In the aerospace and defense industry, deep learning is used to identify objects from satellites that locate areas of interest and identify safe or unsafe zones for troops.

Language recognition

Deep learning machines are beginning to differentiate dialects of a language. Automatic Speech Recognition systems require large amounts of (training) data to achieve qualitative levels of precision and as such, there is a stronger than ever before demand to increase the size of accented speech datasets. 

Translations

Deep learning has helped enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images. It helps computers understand and process language. Translating text from one language to another has improved significantly, thanks to sequence to sequence learning models based on deep recurrent neural networks.
 
Medical Research

Cancer researchers are using deep learning to automatically detect cancer cells. From disease and tumor diagnoses to personalized medicines created specifically for an individual's genome, deep learning is used more and more in the medical and pharmaceutical world.

Industrial Automation

Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.

Electronics

Smart homes use deep learning. Home assistance devices that respond to your voice and know your preferences are powered by deep learning applications.

Virtual Assistants

Intelligent Virtual Assistants - such as Alexa, Siri and Cortana - must be able to quickly understand and respond to the verbal requests of their owners. 
 
Conclusion

Deep Learning is delivering on its promises and Deep Learning models are expected to continue their growth, reach new industries and create even more innovative applications.
 
Deep Learning with Arnia

We have worked on several projects using Deep Learning techniques through our Machine Learning and Artificial Intelligence division, Apsisware. You can read more about our projects, technologies and domains here.  Previous and ongoing projects include and are not limited to prediction and scene comprehension for assisted driving technologies, face recognition, image and sound synthesis, natural language understanding.