A Review on Architecture of Deep Learning

In recent years we have seen an amazing improvement in applications using Deep learning. It started with speech recognition then moved on to computer vision, object recognition and natural language processing. Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. Deep learning are machine learning algorithms based on learning multiple level of abstraction. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. In this study we have done a survey to provide a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.