Learning in the Cloud

2015-04-17
Learning in the Cloud

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  • Author: Mark Warschauer
  • Publisher: Teachers College Press
  • ISBN: 0807770841
  • Category : Education
  • Languages : en
  • Pages : 145

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.

Introduction to Machine Learning in the Cloud with Python

2021-04-28
Introduction to Machine Learning in the Cloud with Python

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  • Author: Pramod Gupta
  • Publisher: Springer Nature
  • ISBN: 3030712702
  • Category : Technology & Engineering
  • Languages : en
  • Pages : 284

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.

Practical Deep Learning for Cloud, Mobile, and Edge

2019-10-14
Practical Deep Learning for Cloud, Mobile, and Edge

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  • Author: Anirudh Koul
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1492034819
  • Category : Computers
  • Languages : en
  • Pages : 585

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Cloud Computing for Machine Learning and Cognitive Applications

2017-06-16
Cloud Computing for Machine Learning and Cognitive Applications

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  • Author: Kai Hwang
  • Publisher: MIT Press
  • ISBN: 026203641X
  • Category : Computers
  • Languages : en
  • Pages : 626

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.

The School in the Cloud

2019-08-14
The School in the Cloud

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  • Author: Sugata Mitra
  • Publisher: Corwin Press
  • ISBN: 1506389163
  • Category : Education
  • Languages : en
  • Pages : 253

The Science and the Story of the Future of Learning Educators have been trying to harness the "promise" of technology in education for decades, to no avail, but we have learned that children in groups—when given access to the Internet—can learn anything by themselves. In this groundbreaking book, you’ll glimpse the emerging future of learning with technology. It turns out the promise isn’t in the technology itself; it’s in the self-directed learning of the children who use it. In 1999, Sugata Mitra conducted the famous "Hole in the Wall" experiment that inspired three TED Talks and earned him the first million-dollar TED prize for research in 2013. Since then, he has conducted new research around self-organized learning environments (SOLE), building "Schools in the Cloud" all over the world. This new book shares the results of this research and offers • Examples of thriving Schools in the Cloud in unlikely places • Mitra’s predictions on the future of learning • How to design assessments for self-organizing learning • How to build your own School in the Cloud • Clips from the documentary, The School in the Cloud Discover the future of learning by digging deep into Mitra’s thought-provoking experiences, examples, and vision.

Building Machine Learning and Deep Learning Models on Google Cloud Platform

2019-09-27
Building Machine Learning and Deep Learning Models on Google Cloud Platform

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  • Author: Ekaba Bisong
  • Publisher: Apress
  • ISBN: 1484244702
  • Category : Computers
  • Languages : en
  • Pages : 703

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Cloud Computing for Teaching and Learning: Strategies for Design and Implementation

2012-04-30
Cloud Computing for Teaching and Learning: Strategies for Design and Implementation

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  • Author: Chao, Lee
  • Publisher: IGI Global
  • ISBN: 1466609583
  • Category : Education
  • Languages : en
  • Pages : 357

With its cost efficiency, enabling of collaboration and sharing of resources, and its ability to improve access, cloud computing is likely to play a big role in the classrooms of tomorrow. Cloud Computing for Teaching and Learning: Strategies for Design and Implementation provides the latest information about cloud development and cloud applications in teaching and learning. The book alsos include empirical research findings in these areas for professionals and researchers working in the field of e-learning who want to implement teaching and learning with cloud computing, as well as provide insights and support to executives concerned with cloud development and cloud applications in e-learning communities and environments.

Machine Learning for Cloud Management

2021-11-25
Machine Learning for Cloud Management

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  • Author: Jitendra Kumar
  • Publisher: CRC Press
  • ISBN: 1000476596
  • Category : Computers
  • Languages : en
  • Pages : 199

Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. Machine learning enabled solutions are the best fit to address these issues as they can analyze and learn from the data. Moreover, it brings automation to the solutions, which is an essential factor in dealing with large distributed systems in the cloud paradigm. Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The predictive approaches are developed using regression-based time series analysis and neural network models. The neural network-based models are primarily trained using evolutionary algorithms, and efficient virtual machine placement schemes are developed using multi-objective genetic algorithms. Key Features: The first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds. Predictive analytics is an integral part of efficient cloud resource management, and this book gives a future research direction to researchers in this domain. It is written by leading international researchers. The book is ideal for researchers who are working in the domain of cloud computing.

Hands-On Machine Learning on Google Cloud Platform

2018-04-30
Hands-On Machine Learning on Google Cloud Platform

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  • Author: Giuseppe Ciaburro
  • Publisher: Packt Publishing Ltd
  • ISBN: 1788398874
  • Category : Computers
  • Languages : en
  • Pages : 489

Unleash Google's Cloud Platform to build, train and optimize machine learning models Key Features Get well versed in GCP pre-existing services to build your own smart models A comprehensive guide covering aspects from data processing, analyzing to building and training ML models A practical approach to produce your trained ML models and port them to your mobile for easy access Book Description Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems. What you will learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google’s pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications Who this book is for This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy

Cloud Computing for Science and Engineering

2017-09-29
Cloud Computing for Science and Engineering

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  • Author: Ian Foster
  • Publisher: MIT Press
  • ISBN: 0262037246
  • Category : Computers
  • Languages : en
  • Pages : 391

A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.