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What is Deep learning?
“Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of Machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.”
Deep learning is inspired by human brain and how it perceives information through interaction of neurons. It’s a branch of Machine Learning and is implemented through large Artificial Neural Networks (> 100 layers). Training ANNs for deep learning requires lots of labeled data as well as huge computing power.
So why “deep” learning? For starters, it requires extensive learning through a large interconnected network. As said by Jurgen Schmidhuber in his paper Deep Learning in Neural Networks: An Overview :
“At which problem depth does Shallow Learning end, and Deep Learning begin? Discussions with Deep learning experts have not yet yielded a conclusive response to this question. […], let me just define for the purposes of this overview: problems of depth > 10 require Very Deep Learning.”
The “deep" also refers to the hundreds of hidden layers of Artificial Neural Networks (ANN) used in Deep learning.
Why you should know about Deep learning?
Nearly every industry is going to be affected by AI and ML and Deep learning play a big role in it. No matter if you are in healthcare or legal, chances are you may get replaced by a highly autonomous robot one day. Deep learning has improved significantly in terms of accuracy over the period of years and is still evolving. Understanding its nuances will help us all.
Some of the wide applications of Deep learning are:
Self Driving cars : A self-driving car is the ultimate evolutionary goal of developing ADASes — Advanced Driver Assistance Systems, to the point when there’s nobody to assist anymore.
Visual tasks, including, but probably not limited to Lane detection, Pedestrian detection, and Road signs recognition, are solved with deep learning.
The importance of deep learning for autonomous driving systems can be illustrated by the fact that Nvidia maintains long-term relationships with car manufacturers, working on embedded and real-time operating systems designed exactly for these purposes.
Humanoids : In a similar fashion, Deep learning is making interacting between robots and humans simpler day by day.
We already have personal agents like Alexa and Siri, that listen to our queries and answer intelligently.
The great advances in NLP and Image processing enabled by Deep learning are the reason behind such efficient interaction.
Looking at the rate of growth of Robotics and Deep learning, autonomous robots are not that far away. A good example being Sophia, a human-like robot by Hanson Robotics.
Healthcare : The adoption of Deep learning in healthcare is on the rise and solving a variety of problems for patients, hospitals and the healthcare industry overall.
Research has shown that Deep Neural Networks can be trained to produce radiological findings with high reliability by training from archives of millions of patient scans collected by healthcare systems.
These kinds of advancements will soon change the health and personal care scenario by replacing doctors with AI empowered expert systems and autonomous robot surgeons.
Space travel : As featured here , Steve Chien and Kiri Wagstaff of NASA’s Jet Propulsion Laboratory have predicted that in the future, the behavior of space probes will be governed by AI rather than human prompts from earth. Again the tremendous ability of Deep learning of finding patterns from raw data comes into play.
Already companies like SpaceX is using the power of AI for sending probes into space. Soon with the help of AI, humans may inhibit other planets!
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