Deep Learning.

Deep Learning is a subfield of machine learning.
It uses complex networks—known as artificial
neural networks — that are inspired by the
design of the human brain.

An Artificial Neural Network

Deep Learning is designed to emulate how the human brain works so that computers can be
trained to deal with abstractions and problems that are poorly defined. Deep Learning is especially
useful when you’re trying to learn patterns from unstructured data.

The word deep in Deep Learning comes from the computational layers that are involved in solving problems. An artificial neural network consists of three or more layers: an input layer, one or many hidden layers, and an output layer. Data is ingested through the input layer. Then the data is modified in the hidden layer and the output layers.

The typical neural network may consist of thousands or even millions of simple processing nodes that are densely interconnected. The term Deep Learning is used when there are multiple hidden layers within a neural network.

Using a repetitive approach, a neural network continuously adjusts and makes inferences until a specific stopping point is reached.

Some of the world’s leading thinkers and developers have been working for decades in the
field of Deep Learning, and it has shown phenomenal results on very difficult tasks such as
recognising objects from an image and understanding speech and languages.

Deep Learning has shown its power in several application areas of Artificial Intelligence, especially in the arena of
computer vision. Computer Vision is the science of understanding and manipulating images, and finds a range of
applications in the areas of robotics, automation, and, of course, surveillance.

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