Demystifying Deep Learning – Back to Basics

This post was originally published at Myblog first on May-30-2018. To find out more about me click here.

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This is part 1 of 2 parts story on DeepLearning & basic terms which revolve (may evolve around as well) around it.

Deep Learning is a very young field, where theories aren’t strongly established and views quickly changes almost on daily basis. Deep Learning is at the cutting edge technology break through. This depicts what machines can do (still very new and at basic level), and developers and business leaders absolutely need to understand what it is and how it works.

“I think people needs to understand that deep learning is making a lot of things, behind the scenes, much better” – Sir Geoffrey Hinton

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Why Deep Learning ?

• Human Brains have a deep architecture.
• Humans organize their ideas hierarchically, through composition of simpler ideas
• Insufficiently deep architectures can be exponentially inefficient.
• Deep architectures facilitate feature and sub-feature sharing.

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What is Deep Learning ?

With lots of noise I can say “Deep learning is undeniably mind-blowing” and “deep learning can be used with too much of ease to predict the unpredictable”. In my personal opinion “We all are so busy in creating artificial intelligence by using combination of non bio neural networks and natural intelligence”.

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Confusing Jargons – AI, ML and many more

“Artificial neural systems, or neural networks, are physical cellular systems which can acquire, store, and utilize experiential knowledge” – Zurada (1992). Like this each one has their own meaning and use cases. One needs to be careful on when to use what for what reasons.

To read the full post click here ....Demystifying key buzzwords like Artificial intelligence, machine learning, artificial neural networks and deep learning is ......