Deep learning has defined as hierarchical learning or deep structured learning. Deep learning is a part of the machine learning methods. And it is based on learning methods, data representations, as against task-specific algorithms. Learning can be in different types of machine learning concepts like supervised, semi-supervised and unsupervised.
In Artificial Intelligence Deep learning methods aim at learning at feature hierarchies with features from higher level features to lower level features. Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directory from data, without depending completely on human-crafted features.
In Deep learning architectures mostly neural networks like trusted networks and recurring neutral networks have been applied to fields including computer vision speech identification of natural language processing audio recognition social network filtering machine translation bioinformatics drug design and board game programs where they have produced.
In Machine Learning Deep learning models are approximately inspired by information processing and communication patterns in organic nervous systems. Coming to structural and functional properties of organic brains which make them opposed with the nervous system.