Big Data and Deep Learning Analytics

Abstract

There has been an enormous growth of the Internet, mobile phone, medical facilities, and many more in the 21st century, which can also be known as the beginning of the knowledge era. Knowledge is defined not for what it is, but for what it can do. In this fast-moving technological era, as a result, a huge amount of data is generated in different regions of the world and it is growing day by day, this growing data is known as “Big Data”. To extract useful information (analyze) from large unstructured data (like Web, sales, customer contact center, social media, mobile data, and so on) is a complex task, as data being generated is a combination of structured, semi-structured and unstructured data. Traditional systems are not capable to handle semi-structured or unstructured data generated whose volume could range in petabytes or exabytes, as the major challenges are limited memory usage, computational hurdles and slower response time, data redundancy, etc. This problem can be overcome with big data analytics having technologies like Apache Hadoop, Apache Spark, Hive, Pig, etc. which can extract useful information from these large data. Authors are going to explore more on them in these chapters. Alongside authors will explore “Deep Learning” also known as “Deep Neural Learning” or “Deep Neural Network”, which is a class of Machine Learning that progressively extract higher-level features from raw data automatically. It performs 'end-to- end learning' and uses layers of algorithms to process data, understand human speech, and visually recognize objects, which is an important part of it. Feature extraction, self-driving cars, fraud detection, healthcare, neural language processing, etc. are some of the areas where it is applied in daily life. Algorithms like RNN, CNN, FNN, Backpropagation, etc, are some of the algorithms used in deep learning. The authors will explore how Machine learning is different from deep learning. Deep learning (DL) is also associated with data science in many ways as the DL algorithms work better than older learning algorithms for prediction or feature extraction etc. Which has brought it, more closer towards one of its main objectives i.e., artificial intelligence (AI)? Hence it is immensely advantageous to the data scientists who aim for making predictions and draw useful information to analyze and interpret it for helping the organization in its growth. The processing of Big Data and the evolution of Artificial Intelligence are both dependent on Deep Learning. Deep learning technology came up along with big data analytics. The concept of deep learning is supportive in the big data analytics due to its efficient use for processing huge and enormous data. This chapter explains about deep learning and big data analytics use in healthcare and alongside authors will study about algorithms used in deep learning and technologies used in big data analytics with its architecture. After reading this chapter, authors must be able to connect deep learning with big data analytics for building new products and contribute to society in a much better way