Machine and Deep Learning Applications

Abstract

In this chapter, we introduce several applications of machine learning and deep learning in different domains, including sensor and time-series, image and vision, text and natural language processing, relational data, energy, manufacturing, social media, health, security, and Internet-of-Things (IoT) applications. Until 2010, traditional ML models such as SVMs and decision trees have enjoyed successes in various tasks, including handwritten digit classification, face detection, and pattern recognition. Though traditional ML models are easy to interpret, the model’s inputs need to be well-designed, handcrafted features. On the other hand, deep learning models circumvent this problem and directly take the raw data as input and provide end-to-end learning capability. There is an unprecedented increase in machine learning and deep learning applications, especially with the emergence of fast mobile devices with access to cloud computing. While cloud computing provides the necessary computational power to train deep learning models, trained models can be easily deployed in the cloud or on embedded devices at the edge of the cloud to carry out the inference.