ABSTRACT
In recent years, deep learning has received huge recognition from almost all knowledge domains. It has delivered groundbreaking success in various important fields like Healthcare, Robotics, Climate Change, Biotechnology, Automatic Driving, NLP, Gaming Technology, Cyber Security, Biomedical Imaging etc. With the availability of resources (such as huge datasets, advanced deep learning frameworks, libraries and high performance computer hardware), deep learning architectures have achieved unprecedented success in predicting and analyzing data for various complex real world problems. DL is a complex sub-field of a class of artificial intelligence called machine learning. Unlike Machine Learning, DL learns multiple levels of data representation using a hierarchical multiple layers architecture with each layer applying its own transformation. Deep Learning as a whole is supervised in nature. Deep learning architectures implemented for various major application domains may be supervised, semi-supervised or unsupervised or Reinforcement Learning. In this chapter, we present a detailed survey and discussion of DL architectures, libraries and frameworks which will be useful for applications in healthcare informatics.
