What exactly is Deep Learning?

Deep Learning is an area of machine-learning that is concerned with algorithms influenced by the nature and function of the brain. These are referred to as artificial neural networks..

If you're just beginning to branch to explore the area of deep learning, or have some experiences with neural networks a while ago, you might be confused. I'm sure I was at first, as were my colleagues as well as friends who had learned about and worked with neural networks during the early 2000s and 1990s.

The top experts and leaders in the field have opinions on the nature of deep learning and these particular and nuanced viewpoints can shed light on the nature of deep learning. about.

In this article this article, you'll discover what deep learning really is through a variety of experts and top leaders within the industry.

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Let's take a dive.

What exactly is Deep Learning?

Deep Learning is Large Neural Networks

Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that ultimately resulted in the development of deep learning technology across many Google services.

He has written and spoken many times about the nature of deep learning and can be a good point to begin.

In his early talks about deep-learning, Andrew discussed deep-learning within its context with traditional neural networks. In his 2013 talk, entitled " Deep Learning, Self-Taught Learning and Unsupervised Features Learning" Andrew discussed the concept of deep learning as:

Through brain simulations, we can hope to:

Learn algorithms to make it more efficient and simpler to use.

Create breakthroughs in machine learning and AI.

I think this is our best chance to make progress towards real AI

His comments later were more specific.

The underlying principle of deep learning, according to Andrew is the fact that we have computers that are fast enough and enough data to build massive neural networks. In discussing why this is the best time for deep learning has taken off, Andrew spoke at ExtractConf 2015 , he gave a talk entitled " What data scientists need to be aware of about deep-learning" He said:

extremely large neural networks that are now available in our arsenal ... enormous quantities of data we can access

He also made the fact that it's all about scaling. When we build large neural networks that we train using ever-growing amounts of data the performance of these networks continues to improve. This is in contrast to other techniques for machine learning which reach a plateau in performance.