Learning Systems
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Teaching To The Brain S Natural Learning Systems
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Author : Barbara K. Given
language : en
Publisher: ASCD
Release Date : 2002
Teaching To The Brain S Natural Learning Systems written by Barbara K. Given and has been published by ASCD this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Education categories.
Uses the brain's five major learning systems--emotional, social, cognitive, physical, and reflective--to provide a framework for designing lessons and determining teaching approaches.
Interactive Learning Systems Evaluation
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Author : Thomas Charles Reeves
language : en
Publisher: Educational Technology
Release Date : 2003
Interactive Learning Systems Evaluation written by Thomas Charles Reeves and has been published by Educational Technology this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
Describes how to evaluate interactive learning systems, both in their initial development and later in regard to effectiveness and efficiency. These include web-based systems, computer-aided learning, etc.
Artificial Intelligence In Education
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Author : Vania Dimitrova
language : en
Publisher: IOS Press
Release Date : 2009
Artificial Intelligence In Education written by Vania Dimitrova and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
Artificial Intelligence in Education conference 2009 (AIED) is part of a series of biennial international conferences for top quality research in intelligent systems and cognitive science for educational computing applications. This title covers papers presented at the Artificial Intelligence in Education conference 2009 (AIED).
Machine Learning Systems
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Author : Jeffrey Smith
language : en
Publisher: Simon and Schuster
Release Date : 2018-05-21
Machine Learning Systems written by Jeffrey Smith and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-21 with Computers categories.
Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence
Foundations Of The Theory Of Learning Systems
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Author : Ja. Z. Cypkin
language : en
Publisher:
Release Date : 1973
Foundations Of The Theory Of Learning Systems written by Ja. Z. Cypkin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1973 with categories.
Architecture Solutions For E Learning Systems
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Author : Pahl, Claus
language : en
Publisher: IGI Global
Release Date : 2007-11-30
Architecture Solutions For E Learning Systems written by Pahl, Claus and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-30 with Computers categories.
"This book provides fundamental research on the architecture of learning technology systems, discussing such issues as the common structures in LTS and solutions for specific forms such as knowledge-based, distributed, or adaptive applications of e-learning. Researchers, and scholars in the fields of learning content software development, computing and educational technologies, and e-learning will find it an invaluable resource"--Provided by publisher.
Social Learning Systems And Communities Of Practice
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Author : Chris Blackmore
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-01
Social Learning Systems And Communities Of Practice written by Chris Blackmore and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-01 with Computers categories.
Social Learning Systems and Communities of Practice is a collection of classical and contemporary writing associated with learning and systemic change in contexts ranging from cities, to rural development to education to nursing to water management to public policy. It is likely to be of interest to anyone trying to understand how to think systemically and to act and interact effectively in situations experienced as complex, messy and changing. While mainly concerned with professional praxis, where theory and practice inform each other, there is much here that can apply at a personal level. This book offers conceptual tools and suggestions for new ways of being and acting in the world in relation to each other, that arise from both old and new understandings of communities, learning and systems. Starting with twentieth century insights into social learning, learning systems and appreciative systems from Donald Schön and Sir Geoffrey Vickers, the book goes on to consider the contemporary traditions of critical social learning systems and communities of practice, pioneered by Richard Bawden and Etienne Wenger and their colleagues. A synthesis of the ideas raised, written by the editor, concludes this reader. The theory and practice of social learning systems and communities of practice appear to have much to offer in influencing and managing systemic change for a better world.
Foundations Of The Theory Of Learning Systems
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Author : I︠A︡kov Zalmanovich T︠S︡ypkin
language : en
Publisher:
Release Date : 1973
Foundations Of The Theory Of Learning Systems written by I︠A︡kov Zalmanovich T︠S︡ypkin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1973 with Psychology categories.
Foundations of the theory of learning systems.
Online Learning Systems
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Author : Zdzislaw Polkowski
language : en
Publisher: CRC Press
Release Date : 2023-02-10
Online Learning Systems written by Zdzislaw Polkowski and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-10 with Business & Economics categories.
This book discusses the newest approach to online learning systems in higher education. As e-Learning platforms change their mechanisms for data processing and storage, there is a need to move these systems toward being more efficient and smarter. This book covers online learning systems and their application to large-scale data along with the technological aspects of these processes and problem-solving methods. Online Learning Systems: Methods and Applications with Large-Scale Data discusses the efficiency measurement and environmental impact of online education. The book offers a parametric evaluation and categorization of online learning systems and provides an exploration of big data ecosystems in cloud computing. Descriptive analytical methods that assist in finding solutions for big data challenges are also covered within the book. The book is written for academicians, which includes teachers, students, and higher education policymakers who believe in transforming the education industry, as well as research scholars and those working in education technology and artificial intelligence. Industry professionals involved in education management and those working in e-Learning companies will also find this book useful.
Neural Symbolic Learning Systems
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Author : Artur S. d'Avila Garcez
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Symbolic Learning Systems written by Artur S. d'Avila Garcez and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.