Deep learning is a much focused domain of artificial neural networks. Deep learning algorithms try to learn massive amounts of unlabelled data and make a better analysis. With deep learning, all layers learn the input data and transform it into a more abstract and composite format. The word “deep” means higher numbers of hidden layers in which the data from one layer to another is transformed to generate the most accurate outcome. Deep learning architecture has been applied to different fields like medical image analysis, machine translation, bioinformatics, speech recognition, social network filtering, computer vision, audio recognition drug design, natural language processing, and so on. This chapter discusses important deep learning applications across different disciplines, their contribution to the real world, and a study of the architectures and methods used by each application. This chapter also introduces the differences between machine learning and deep learning. Finally, this chapter concludes with future aspects and conclusions.