Deep Learning Analytics

Abstract

The recent breakthroughs in Deep Learning have provided powerful data analytics tools for a wide range of domains ranging from advertising and analyzing users’ behavior to load and financial forecasting. Depending on the nature of the available data and the task at hand Deep Learning Analytics techniques can be divided into two broad categories: (a) unsupervised learning techniques and (b) supervised learning techniques. In this chapter we provide an extensive overview over both categories. Unsupervised learning methods, such as Autoencoders, are able to discover and extract the information from the data without using any ground truth information and/or supervision from domain experts. Thus, unsupervised techniques can be especially useful for data exploration tasks, especially when combined with advanced visualization techniques. On the other hand, supervised learning techniques are used when ground truth information is available and we want to build classification and/or forecasting models. Several deep learning models are examined ranging from simple Multilayer Perceptrons (MLPs) to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). However training deep learning models is not always a straightforward task requiring both a solid theoretical background as well as intuition and experience. To this end, we also present recent techniques that allow for efficiently training deep learning models, such as batch normalization, residual connections, advanced optimization techniques and activation functions, as well as a number of useful practical suggestions. Finally, we present an overview of the available open source deep learning frameworks that can be used to implement deep learning analytics techniques and accelerate the training process using Graphics Processing Units (GPUs).