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Deep Learning is a type of Neural Network Algorithm that takes metadata as an input and process the data through a number of layers of the non-linear transformation of the input data to compute the output.

This algorithm has a unique feature i.e. automatic feature extraction. This means that this algorithm automatically grasps the relevant features required for the solution of the problem.

This reduces the burden on the programmer to select the features explicitly. This can be used to solve supervised, unsupervised or semi-supervised type of problems.

In Deep Learning Neural Network, each hidden layer is responsible for training the unique set of features based on the output of the previous layer. As the number of hidden layers increases, the complexity and abstraction of data also increase.

It forms a hierarchy from low-level features to high-level features. With this, it becomes possible that Deep Learning Algorithm can be used to solve higher complex problems consisting of a large number of non-linear transformational layers.

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