Chapter 3 - An overview of deep learning in big data, image, and signal processing in the modern digital age

Abstract

Nowadays, data is generated all the time on the internet. Faced with this scenario, technologies have emerged to take advantage of this feature, so that in addition to just being able to measure and understand where they come from, it is possible for them to be collected, quantified, decoded, and analyzed, allowing the understanding of behaviors and trends, the definition of strategies, and the process of insight generation. Big data leverages resources that organize and catalog this information, increasing the availability of relevant data for informed decision making. Machine learning is an aspect of artificial intelligence that competently performs automation in the process of building analytical models that allow machines to adapt independently to new scenarios, enabling software to successfully predict and react to the deployment of scenarios based on past results. Deep learning has this nomenclature because it deals with neural networks having multiple (deep) layers that allow learning; therefore it is a subset of machine learning, which considers algorithms inspired by the human brain, the artificial neural networks, which learn from large amounts of data. Deep learning techniques are especially useful for analyzing complex, rich, and multidimensional data such as voice, images, and videos. In short, all deep learning is machine learning, but not all machine learning is deep learning. This chapter examines the technology of deep learning and machine learning in big data by addressing its evolution and fundamental concepts and its integration into new technologies, by approaching its success, and by categorizing and synthesizing the potential of both technologies.