Author(s)

Haoran Han

Corresponding Author

Haoran Han

ABSTRACT

As the development of the big data era, features are able to be extracted from an increasing amount of data. Deep learning provides an approach for machines to classify features more precisely over a large amount of data. This paper investigates methods for deep learning and their applications. The overviewed algorithms convolutional networks, stochastic slope plunge techniques (SGDs) and computer vision-based deep learning algorithms in different application scenarios such as computer vision, human robot interaction and medicine. This paper draws the conclusions that deep learning is an essential solution to find closed-to-optimal solutions in complex environments with a large number of training sets available.

KEYWORDS

deep learning, machine learning, artificial intelligence