Learning In The Cloud
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Learning in the Cloud
This comprehensive and cutting-edge book portrays a vision of how digital media can help transform schools, and what kinds of curriculum pedagogy, assessment, infrastructure, and learning environments are necessary for the transformation to take place. The author and his research team spent thousands of hours observing classes and interviewing teachers and students in both successful and unsuccessful technology-rich schools throughout the United States and other countries. Featuring lessons learned as well as analysis of the most up-to-date research, they offer a welcome response to simplistic approaches that either deny the potential of technology or exaggerate its ability to reform education simply by its presence in schools. Challenging conventional wisdom about technology and education, Learning in the Cloud: critically examines concepts such as the "digital divide," "21st-century skills," and "guide on the side" for assessing and guiding efforts to improve schools; combines a compelling vision of technology's potential to transform learning with an insightful analysis of the curricular challenges required for meaningful change; and discusses the most recent trends in media and learning, such as the potential of tablets and e-reading.
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.
Introduction to Machine Learning in the Cloud with Python
This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.