Why Is Deep Learning Hot Right Now

We all have came across this hot-word at least once, especially because recently a lot of tech companies are popularising this field and showcasing it's abilities to solve all kind of problems which we considered impossible till now.

So what exactly is this "Deep Learning" that everyone is talking about?

In simple words the Deep Learning can be referred to as the creation of machines which uses techniques and abilities inspired by the human brains ability to learn.

So how exactly does this Deep Learning work?

Up until recently  we simply didn't have enough data to make machines learn like a human.

But now with sufficient data and processing power we can train a computer to learn using Deep Neural Networks. These Deep Learning Neural Networks works on many levels of abstraction. They range from simple to complex ones. This is what puts "Deep" in the "Deep Learning".

Each layer categorises some form of information, refines it and passes it along the next level. Deep Learning lets machine use this process to build a hierarchical representation.

Traditional Machine learning uses handwritten feature extraction and modality-specific machine learning algorithms to label images or recognise voices. However this method has several drawbacks in both time-to-solution and accuracy.

Today's advanced deep neural networks use algorithms, big data and the computational powers to change this dynamics. Machines are now able to learn at a speed, accuracy, and scale that are driving true artificial intelligence and AI Computing.

So why should we be concerned about this field?

Deep Learning has countless applications ranging from medical diagnosis, robotics, automotive safety to text, facial and speech recognition. Deep Learning is used in the research community and in industry to help solve many problems.

Practical examples include:

• Vehicle, pedestrian and landmark identification for driver assistance
• Image recognition
• Speech recognition and translation
• Natural language processing
• Life Sciences

So what now?

Even after all these years we are just scratching the surface of this field. That's why researchers at top universities worldwide and a host of start ups are pushing to put Deep Learning to work.