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Author: Tanya Ovenden-Hope
Publisher: Routledge
ISBN: 1317434692
Category : Education
Languages : en
Pages : 145
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Book Description
Understanding Applied Learning enables teachers, lecturers and educators to facilitate applied learning effectively with learners in schools, colleges and universities. It introduces teachers to the concept of applied learning in practice, cutting across any vocational and academic divide to show how this approach supports high-quality and effective outcomes for learners. Applied learning prepares and equips learners for life in the twenty-first century and lifelong learning. Offering practical guidance on why and how to adopt applied learning in all post-primary settings, this practical resource introduces and explores the core concepts, practices and benefits of using this approach. Illustrated with real-life scenarios, it examines why applied learning is relevant today, how it enables learners to connect knowledge with new situations, how to navigate and solve intellectual and skills-based problems and how to work collaboratively and develop higher-level thinking skills. Key topics covered include: A range of applied learning theories and strategies Relevant, Engaging, Active Learning (REAL) for successful knowledge and skills development The relevance of applied learning to employers Overcoming issues in embedding applied learning approaches How to embed creativity into learning experiences. Understanding Applied Learning is an authoritative, down-to-earth guide to facilitate applied learning effectively and successfully with students in secondary schools, colleges and universities. It is a source of support and inspiration for all those committed to high-quality and effective outcomes for learners.
Author: Tanya Ovenden-Hope
Publisher: Routledge
ISBN: 1317434692
Category : Education
Languages : en
Pages : 145
Get Book Here
Book Description
Understanding Applied Learning enables teachers, lecturers and educators to facilitate applied learning effectively with learners in schools, colleges and universities. It introduces teachers to the concept of applied learning in practice, cutting across any vocational and academic divide to show how this approach supports high-quality and effective outcomes for learners. Applied learning prepares and equips learners for life in the twenty-first century and lifelong learning. Offering practical guidance on why and how to adopt applied learning in all post-primary settings, this practical resource introduces and explores the core concepts, practices and benefits of using this approach. Illustrated with real-life scenarios, it examines why applied learning is relevant today, how it enables learners to connect knowledge with new situations, how to navigate and solve intellectual and skills-based problems and how to work collaboratively and develop higher-level thinking skills. Key topics covered include: A range of applied learning theories and strategies Relevant, Engaging, Active Learning (REAL) for successful knowledge and skills development The relevance of applied learning to employers Overcoming issues in embedding applied learning approaches How to embed creativity into learning experiences. Understanding Applied Learning is an authoritative, down-to-earth guide to facilitate applied learning effectively and successfully with students in secondary schools, colleges and universities. It is a source of support and inspiration for all those committed to high-quality and effective outcomes for learners.
Author: Tom Vander Ark
Publisher: John Wiley & Sons
ISBN: 1118115872
Category : Education
Languages : en
Pages : 240
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Book Description
A comprehensive look at the promise and potential of online learning In our digital age, students have dramatically new learning needs and must be prepared for the idea economy of the future. In Getting Smart, well-known global education expert Tom Vander Ark examines the facets of educational innovation in the United States and abroad. Vander Ark makes a convincing case for a blend of online and onsite learning, shares inspiring stories of schools and programs that effectively offer "personal digital learning" opportunities, and discusses what we need to do to remake our schools into "smart schools." Examines the innovation-driven world, discusses how to combine online and onsite learning, and reviews "smart tools" for learning Investigates the lives of learning professionals, outlines the new employment bargain, examines online universities and "smart schools" Makes the case for smart capital, advocates for policies that create better learning, studies smart cultures
Author: Donnelly, Roisin
Publisher: IGI Global
ISBN: 1599048175
Category : Business & Economics
Languages : en
Pages : 440
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Book Description
"This book presents international practices in the development and use of applied e-Learning and e-Teaching in the classroom in order to enhance student experience, add value to teaching practices, and illuminate best practices in the area of e-Assessment. This book provides insight into e-Learning and e-Teaching practices while exploring the roles of academic staff in adoption and application"--Provided by publisher.
Author: Pethuru Raj Chelliah
Publisher: CRC Press
ISBN: 1000461351
Category : Computers
Languages : en
Pages : 369
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Book Description
This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights. This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.
Author: Railean, Elena
Publisher: IGI Global
ISBN: 1466696354
Category : Education
Languages : en
Pages : 1049
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Book Description
The field of education is in constant flux as new theories and practices emerge to engage students and improve the learning experience. Research advances help to make these improvements happen and are essential to the continued improvement of education. The Handbook of Research on Applied Learning Theory and Design in Modern Education provides international perspectives from education professors and researchers, cyberneticists, psychologists, and instructional designers on the processes and mechanisms of the global learning environment. Highlighting a compendium of trends, strategies, methodologies, technologies, and models of applied learning theory and design, this publication is well-suited to meet the research and practical needs of academics, researchers, teachers, and graduate students as well as curriculum and instructional design professionals.
Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 595
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Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Author: David Forsyth
Publisher: Springer
ISBN: 3030181146
Category : Computers
Languages : en
Pages : 496
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Book Description
Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:• classification using standard machinery (naive bayes; nearest neighbor; SVM)• clustering and vector quantization (largely as in PSCS)• PCA (largely as in PSCS)• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)• linear regression (largely as in PSCS)• generalized linear models including logistic regression• model selection with Lasso, elasticnet• robustness and m-estimators• Markov chains and HMM’s (largely as in PSCS)• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy• simple graphical models (in the variational inference section)• classification with neural networks, with a particular emphasis onimage classification• autoencoding with neural networks• structure learning
Author: Dawn A. Morley
Publisher: Springer Nature
ISBN: 3030469514
Category : Education
Languages : en
Pages : 425
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Book Description
This open access book critiques real world learning across both the curriculum and extracurricular activities. Drawing on disciplines as diverse as business, health, fashion, sociology and geography, the editors and authors employ a cross-disciplinary approach to examine how this concept is being applied in higher education. Divided into three parts, the authors and contributors analyse broader applications of real world learning, student experience of practicing in a real world setting, and how learning strategies can be employed to engage students in real world learning. The editors and contributors provide up-to-date, cross-disciplinary and international insights into how real world learning could be integrated into the higher education curriculum to support effective, relevant and life-long learning for 21st century students.
Author: Umberto Michelucci
Publisher: Apress
ISBN: 1484237900
Category : Computers
Languages : en
Pages : 425
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Book Description
Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will Learn Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset Who This Book Is For Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.
Author: Loui MCINTOSH
Publisher:
ISBN: 9783038602446
Category :
Languages : en
Pages : 220
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Book Description
A unique and fascinating transcultural study on the role of imagery and appropriation in architecture and urban planning. Founded by Swiss settlers in 1845, New Glarus in Wisconsin evolved from being a dairy farming and cheese production village to a popular tourist destination. Following a grave economic downturn in the 1960s and 1970s, the community discovered embracing the image of its cultural heritage, particularly traditional architectural details, as a way of survival. Consequently, they began to change their commercial building façades to appear even more Swiss. Since 1999, the town has even regulated the production of new buildings via its building codes to preserve this particular aesthetic evoking the familiar traditional Swiss chalet style. Swissness Applied investigates the transformation of European immigrant towns in the United States, exemplified by New Glarus. It features the results of extensive fieldwork on buildings in the village as well as design projections based on the local building code and evaluates the outcomes through different representation techniques. Expert authors including Courntey Coffman, Kurt Forster, Whitney Moon, Philip Ursprung, and Jesús Vassallo contribute essays that pick up on aspects such as the role of cultural imagery and immigration history in architecture, and on Swissness as a cultural concept in particular.
