Cloud Computing for Machine Learning and Cognitive Applications
The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies.
This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data.
This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science.
Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.
Downloadable instructor resources available for this title: solutions, slides, and file of figures in the book
Hardcover$115.00 X ISBN: 9780262036412 624 pp. | 8 in x 9 in 273 b&w illus., 98 tables
An essential and tested text with problems and design projects that will become a critical reference. Kai Hwang covers the stack from the service models; through the Internet of Things; to the analytics and machine learning afforded by the cloud.
Researcher Emeritus, Microsoft
The book is a great resource for all those interested in learning about the cross-fertilization between a number of topics: clouds, machine learning, security and data privacy, and mobility and the cloud, among many other topics. A welcome addition to the cloud computing literature.
Albert Y. Zomaya
Professor, Sydney University; coeditor of Handbook on Data Centers