Intergenerational Learning

These easy-carry learning pads include everything you need to reinforce key school topics while on the go! Includes more than 100 stickers!This interactive carry handle learning pad focuses on critica

These easy-carry learning pads include everything you need to reinforce key school topics while on the go! Includes more than 100 stickers!This interactive carry handle learning pad focuses on critica

Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and res

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.Today, machine learning underlies a range of app

此系列共7冊:180 Days of Social-Emotional Learning for Kindergarten180 Days of Social-Emotional Learning for First Grade180 Days of Social-Emotional Learning for Second Grade180 Days of Social-Emotional Learning for Third Grade180 Days of Social-Emotional Learning for Fourth Grade180 Days of Social-Emotional Learning for Fifth Grade180 Days of Social-Emotional Learning for Sixth Grade

With more than 60 tear-away pages of engaging activities, this Kindergarten Learning Pad helps Kindergarteners find school success. Includes activities focusing on the alphabet, phonics, math, science

Priddy Learning: My First Animals is a first novelty book packed full of early learning fun, ideal for toddlers.Preschoolers can explore different habitats and discover all the amazing animals that li

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group.You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you've been curious about machine learning but didn't k

A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the nee

Coon, Mitterer and Martini's bestselling PSYCHOLOGY: MODULES FOR ACTIVE LEARNING, 15th Edition combines clear learning objectives, an engaging style, appealing visuals and detailed coverage of core to

Looking for Learning: Mark Making is a full-colour, practical guide to inspire child-led learning that's linked to current policy and the EYFS framework. As each child progresses through their learnin

Looking for Learning: Maths Through Play is a full-colour, practical guide to inspire child-led learning that's linked to current policy and the EYFS framework. As each child progresses through their

The world of machine learning is changing all the time. It is so amazing the idea that we are able to take a computer and let it learn as it goes. Without having to write out all of the codes that we need for every situation out there or every input that the user may pick, we are able to write out codes in machine learning, even with Python, in order to let the computer or device learn and make decisions on its own.This guidebook is going to take a closer look at how Python machine learning is able to work, as well as how you can use some of the tools and techniques that come with this process for your own needs. When you are interested in learning more about what machine learning is all about, as well as how you can use a part of the coding from Python inside of this process, then this guidebook is the tool for you Some of the topics that we will explore when we go through this guidebook will include: Understanding some of the basics of machine learning;Some of the different parts tha

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli

This volume offers a historical and critical analysis of the emerging field of the learning sciences, which takes an interdisciplinary approach to understanding and improving how children and adults learn. It features a wide range of authors, including established scholars who founded and guided the learning sciences through the initial turbulence of forming a new line of academic inquiry, as well as newcomers who are continuing to shape the field. This diversity allows for a broad yet selective perspective on what the learning sciences are, why they came to be, and how contributors conduct their work. Reflections on the Learning Sciences serves both as a starting point for discussion among scholars familiar with the discipline and as an introduction for those interested in learning more. It will benefit graduate students and researchers in computer science, educational psychology, instructional technology, science, engineering, and mathematics.

A comprehensive and integrated introduction to the phenomena and theories of perceptual learning, focusing on the visual domain.Practice or training in perceptual tasks improves the quality of percept

Today, more people want to know how to make a meaningful difference to what they care about. But for that, traditional approaches to learning often fall short. In this book, we offer a theoretical and practical way forward. We introduce the concept of social learning spaces for developing both new capabilities and a sense of agency. We provide a rich framework for focusing on the value of social learning spaces: how to generate this value, monitor it, and learn iteratively through the process. The book is a useful extension and refinement of 'communities of practice' for those familiar with the theory. For those who are not, the chapters will lay out a new way to approach learning. This volume is written to serve the needs of readers across fields, including researchers, educators, and leaders in business, government, healthcare, and international development.

Today, more people want to know how to make a meaningful difference to what they care about. But for that, traditional approaches to learning often fall short. In this book, we offer a theoretical and practical way forward. We introduce the concept of social learning spaces for developing both new capabilities and a sense of agency. We provide a rich framework for focusing on the value of social learning spaces: how to generate this value, monitor it, and learn iteratively through the process. The book is a useful extension and refinement of 'communities of practice' for those familiar with the theory. For those who are not, the chapters will lay out a new way to approach learning. This volume is written to serve the needs of readers across fields, including researchers, educators, and leaders in business, government, healthcare, and international development.

Bringing together an international team of researchers, this volume explores practice in second language learning - activities which aim to develop skills in or knowledge of a second language. The book begins with two theoretical overviews of practice as applied to learning to speak in a second language and in cognitive accounts of second language acquisition. This theory underpins the volume, which is split into two main sections: receptive and productive practice. The studies look at practice in English, German and Spanish as a second language in various contexts including traditional classrooms, periods of study abroad and online language learning. The differing research designs used mean that the chapters contain clear implications for classroom pedagogy and further directions for research, teaching and learning in different contexts.

This book puts the passion and depth back into how teachers plan for learning in the primary classroom. Offering a unique perspective on what constitutes purposeful planning for learning, this book en

This book puts the passion and depth back into how teachers plan for learning in the primary classroom. Offering a unique perspective on what constitutes purposeful planning for learning, this book en

Informal Learning, Practitioner Inquiry and Occupational Education explores how practitioners in a variety of occupations perform their jobs and argues that working and learning are intricately connec

This book explores learning in the arts and highlights ways in which art and creativity can ignite learning in schools, informal learning spaces, and higher education. The focus is on learning in, wit

An introduction to the psychology of learning that summarizes and integrates findings from both functional psychology and cognitive psychology.learning.Learning unites all living creatures, from simpl

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

Advances in computer graphic technologies have inspired new efforts to understand the potential of multimedia instruction as a means of promoting human learning. In Multimedia Learning, Third Edition, Richard E. Mayer takes an evidence-based approach to improving education using well-designed multimedia instruction. He reviews 15 principles of multimedia instructional design that are based on more than 200 experimental research studies and grounded in a cognitive theory of how people learn from words and graphics. The result is the latest instalment of what Mayer calls the Cognitive Theory of Multimedia Learning, a theory introduced in previous editions of Multimedia Learning and in The Cambridge Handbook of Multimedia Learning, Second Edition. This edition provides an up-to-date and systematic summary of research studies on multimedia learning, supplemented with complementary evidence from around the globe. It is well-suited to graduate and undergraduate courses in psychology, educati

Advances in computer graphic technologies have inspired new efforts to understand the potential of multimedia instruction as a means of promoting human learning. In Multimedia Learning, Third Edition, Richard E. Mayer takes an evidence-based approach to improving education using well-designed multimedia instruction. He reviews 15 principles of multimedia instructional design that are based on more than 200 experimental research studies and grounded in a cognitive theory of how people learn from words and graphics. The result is the latest instalment of what Mayer calls the Cognitive Theory of Multimedia Learning, a theory introduced in previous editions of Multimedia Learning and in The Cambridge Handbook of Multimedia Learning, Second Edition. This edition provides an up-to-date and systematic summary of research studies on multimedia learning, supplemented with complementary evidence from around the globe. It is well-suited to graduate and undergraduate courses in psychology, educati

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

100 Ideas for Primary Teachers: Outdoor Learning is packed with inspiring ideas to generate enthusiasm and engage pupils in learning through positive outdoor experiences. These can then be used as a s

Online learning is ubiquitous for millions of students worldwide, yet our understanding of student experiences in online learning settings is limited. The geographic distance that separates faculty fr

Implementing evidence-based and innovative teaching practices can feel like juggling: If you have standards-based learning in one hand and social-emotional learning in the other, what do you do with c

Around the world, children embark on learning to read in their home language or writing system. But does their specific language, and how it is written, make a difference to how they learn? How is learning to read English similar to or different from learning in other languages? Is reading alphabetic writing a different challenge from reading syllabic or logographic writing? Learning to Read across Languages and Writing Systems examines these questions across seventeen languages representing the world's different major writing systems. Each chapter highlights the key features of a specific language, exploring research on learning to read, spell, and comprehend it, and on implications for education. The editors' introduction describes the global spread of reading and provides a theoretical framework, including operating principles for learning to read. The editors' final chapter draws conclusions about cross-linguistic universal trends, and the challenges posed by specific languages and

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Blended learning offers educators the opportunity to reimagine teaching and learning. It allows teachers to partner with their students to assess, track, and reflect on learning. This partnership give

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha