Systems That Learn

Systems That Learn PDF

Author: Daniel N. Osherson

Publisher: Bradford Books

Published: 1990

Total Pages: 205

ISBN-13: 9780262650243

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Systems That Learn presents a mathematical framework for the study of learning in a variety of domains. It provides the basic concepts and techniques of learning theory as well as a comprehensive account of what is currently known about a variety of learning paradigms.Daniel N. Osherson and Scott Weinstein are at MIT, and Michael Stob at Calvin College.

Systems that Learn

Systems that Learn PDF

Author: Sanjay Jain

Publisher: MIT Press

Published: 1999

Total Pages: 346

ISBN-13: 9780262100779

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This introduction to the concepts and techniques of formal learning theory is based on a number-theoretical approach to learning and uses the tools of recursive function theory to understand how learners come to an accurate view of reality.

Computer Systems that Learn

Computer Systems that Learn PDF

Author: Sholom M. Weiss

Publisher: Morgan Kaufmann Publishers

Published: 1991

Total Pages: 248

ISBN-13:

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This text is a practical guide to classification learning systems and their applications, which learn from sample data and make predictions for new cases. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's point of view.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow PDF

Author: Aurélien Géron

Publisher: "O'Reilly Media, Inc."

Published: 2019-09-05

Total Pages: 851

ISBN-13: 149203259X

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

School Systems That Learn

School Systems That Learn PDF

Author: Paul B. Ash

Publisher: Corwin Press

Published: 2012-12-04

Total Pages: 209

ISBN-13: 1452271976

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When school systems learn, professional practice improves and student achievement increases Picture this: Teachers sharing insights and challenges. Principals leading with trust. Central office leaders inspiring and supporting principals. A synergistic learning system that results in all students succeeding. This practitioner′s guide to creating a system-wide learning organization focuses on professional learning as the stimulus to improving student achievement. Experienced superintendents Paul Ash and John D′Auria provide a blueprint to: Improve schools through system-wide professional learning Increase student achievement by instilling a deep-rooted culture of curiosity Bolster faculty and staff morale with trust-building initiatives Align professional development with student-centered district standards School Systems That Learn shows how professional development in a K-12 district can create synergy between educators and students that results in growth and achievement for all! "Paul Ash and John D′Auria draw on their deep understanding of school districts to help explain why so many American students are left behind. Their solution—to build the capacity of educators through collaboration and honest reflection—should make their book required reading for anyone who aspires to educational leadership." —Karin Chenoweth, Co-author of Getting It Done: Leading Academic Success in Unexpected Schools "This is a carefully developed and immensely practical guide for educators on how to build trust, develop collaborative capacity, and foster leadership at all levels—from the classroom teacher to the superintendent." —Amy C. Edmondson, Novartis Professor of Leadership and Management, Harvard Business School Author of Teaming: How Organizations Learn, Innovate and Compete in the Knowledge Economy

The Learning Healthcare System

The Learning Healthcare System PDF

Author: Institute of Medicine

Publisher: National Academies Press

Published: 2007-06-01

Total Pages: 374

ISBN-13: 0309133939

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As our nation enters a new era of medical science that offers the real prospect of personalized health care, we will be confronted by an increasingly complex array of health care options and decisions. The Learning Healthcare System considers how health care is structured to develop and to apply evidence-from health profession training and infrastructure development to advances in research methodology, patient engagement, payment schemes, and measurement-and highlights opportunities for the creation of a sustainable learning health care system that gets the right care to people when they need it and then captures the results for improvement. This book will be of primary interest to hospital and insurance industry administrators, health care providers, those who train and educate health workers, researchers, and policymakers. The Learning Healthcare System is the first in a series that will focus on issues important to improving the development and application of evidence in health care decision making. The Roundtable on Evidence-Based Medicine serves as a neutral venue for cooperative work among key stakeholders on several dimensions: to help transform the availability and use of the best evidence for the collaborative health care choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and, ultimately, to ensure innovation, quality, safety, and value in health care.

Social Learning Systems and Communities of Practice

Social Learning Systems and Communities of Practice PDF

Author: Chris Blackmore

Publisher: Springer Science & Business Media

Published: 2010-06-01

Total Pages: 231

ISBN-13: 1849961336

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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.

Automated Machine Learning

Automated Machine Learning PDF

Author: Frank Hutter

Publisher: Springer

Published: 2019-05-17

Total Pages: 223

ISBN-13: 3030053180

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This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.