Advanced Analytics and Deep Learning Models

7Deep Learning Methods for Data Science

K. Indira¹, Kusumika Krori Dutta¹*, S. Poornima² and Sunny Arokia Swamy Bellary³

¹M.S. Ramaiah Institute of Technology, Bengaluru, India

²Anna University, Chennai, India

³Charlotte, NC, United States

Abstract

Deep learning network (DLN) is defined as the neural network characterized by complex connected layers to handle a large volume of data, automatic extraction of features, and representation learning for identification and regression problems. This concise chapter on deep learning (DL) methods for data science takes readers through a series of program-writing tasks that introduce them to the use of different DL techniques in various areas of artificial intelligence (AI). It covers zen and tao of the various types of DL methods such as convolutional neural network, recurrent neural network (RNN), denoising autoencoder (DAE), recursive neural network, deep reinforcement learning, deep belief networks (DBNs), and long short-term memory (LSTM), i.e., starting from architecture, learning rules, mathematical model to programing aspects explained in this chapter. The developed and emerging structures of DLN has been applied in applications according to the depth of computational graph, learning, and performance. The knowledge of merits and demerits of each method can train reader toward selection of best suited technique for a given problem statement. For example, the evolution of RNN-based DL architecture innovated many applications in ...