  In the recent time deep learning has achieved huge popularity due to its
performance in various machine learning algorithms. Deep learning as
hierarchical or structured learning attempts to model high level abstractions
in data by using a group of processing layers. The foundation of deep learning
architectures is inspired by the understanding of information processing and
neural responses in human brain. The architectures are created by stacking
multiple linear or non-linear operations. The article mainly focuses on the
state-of-art deep learning models and various real world applications specific
training methods. Selecting optimal architecture for specific problem is a
challenging task, at a closing stage of the article we proposed optimal
approach to deep convolutional architecture for the application of image
recognition.
