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With Iterative Learning Control Algorithms and Experimental Benchmarking the authors discuss the main methods of iterative learning control (ILC) and its interactions, as well as comparator performanc

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves as both a starting point for researchers and graduate students in mathematics trying to get into the field, as well as an invaluable reference for future research.

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slide

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

This early-learning workbook is full of fun activities to help develop your child's fine motor skills.Improve your preschooler's fine-motor skills with Priddy Learning: Scissor Skills, a fun activity book from Priddy Books.This 80-page paperback workbook is designed to encourage safe scissor practice, with varying activities to engage children aged three and over. The exercises increase in difficulty as you go through the book and also include ideas for pasting and creating your own pictures so there is something for every ability level.Each page is perforated which helps for easy-cutting and so that your child's proud artwork can be displayed afterwards. Activities such as shapes, puzzles, mazes, and more are bright and appealing and the quality paper is easy for children to handle and cut without tearing.Part of the Priddy Learning Workbook series which includes; Priddy Learning Tracing & Pen Control, Priddy Learning: Lowercase Alphabet, Priddy Learning Uppercase Alphabet, Priddy

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences, first published in 2006, is the definitive introduction to this innovative approach to teaching, learning, and educational technology. In this significantly revised third edition, leading scholars incorporate the latest research to provide seminal overviews of the field. This research is essential in developing effective innovations that enhance student learning - including how to write textbooks, design educational software, prepare effective teachers, and organize classrooms. The chapters illustrate the importance of creating productive learning environments both inside and outside school, including after school clubs, libraries, and museums. The Handbook has proven to be an essential resource for graduate students, researchers, consultants, software designers, and poli

Evolution of Learning and Memory Mechanisms is an exploration of laboratory and field research on the many ways that evolution has influenced learning and memory processes, such as associative learning, social learning, and spatial, working, and episodic memory systems. This volume features research by both outstanding early-career scientists as well as familiar luminaries in the field. Learning and memory in a broad range of animals are explored, including numerous species of invertebrates (insects, worms, sea hares), as well as fish, amphibians, birds, rodents, bears, and human and nonhuman primates. Contributors discuss how the behavioral, cognitive, and neural mechanisms underlying learning and memory have been influenced by evolutionary pressures. They also draw connections between learning and memory and the specific selective factors that shaped their evolution. Evolution of Learning and Memory Mechanisms should be a valuable resource for those working in the areas of experiment

Evolution of Learning and Memory Mechanisms is an exploration of laboratory and field research on the many ways that evolution has influenced learning and memory processes, such as associative learning, social learning, and spatial, working, and episodic memory systems. This volume features research by both outstanding early-career scientists as well as familiar luminaries in the field. Learning and memory in a broad range of animals are explored, including numerous species of invertebrates (insects, worms, sea hares), as well as fish, amphibians, birds, rodents, bears, and human and nonhuman primates. Contributors discuss how the behavioral, cognitive, and neural mechanisms underlying learning and memory have been influenced by evolutionary pressures. They also draw connections between learning and memory and the specific selective factors that shaped their evolution. Evolution of Learning and Memory Mechanisms should be a valuable resource for those working in the areas of experiment

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod

In Deep Learning with Python, Second Edition, updated from the original bestseller with over 50% new content, you'll explore challenging concepts and practice applications in computer vision, natural-language processing, and generative models. The bestseller revised Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library, written by the creator of Keras himself. This revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. In Deep Learning with Python, Second Edition, updated from the original bestseller with over 50% new content, you'll explore challenging concepts and practice applications in computer vision, natural-language processing, and generative models, building your understanding through practical examples and intuitive explanations that make the complexities of deep learning easily accessible. Purchase of the print book

Machine learning-a computer's ability to learn-is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well.Introduction to Machine Learning weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kep

Digital and online learning is more prevalent than ever, making multimedia learning a primary objective for many instructors. The Cambridge Handbook of Multimedia Learning examines cutting-edge research to guide creative teaching methods in online classrooms and training. Recognized as the field's major reference work, this research-based handbook helps define and shape this area of study. This third edition provides the latest progress report from the world's leading multimedia researchers, with forty-six chapters on how to help people learn from words and pictures, particularly in computer-based environments. The chapters demonstrate what works best and establishes optimized practices. It systematically examines well-researched principles of effective multimedia instruction and pinpoints exactly why certain practices succeed by isolating the boundary conditions. The volume is founded upon research findings in learning theory, giving it an informed perspective in explaining precisely

Digital and online learning is more prevalent than ever, making multimedia learning a primary objective for many instructors. The Cambridge Handbook of Multimedia Learning examines cutting-edge research to guide creative teaching methods in online classrooms and training. Recognized as the field's major reference work, this research-based handbook helps define and shape this area of study. This third edition provides the latest progress report from the world's leading multimedia researchers, with forty-six chapters on how to help people learn from words and pictures, particularly in computer-based environments. The chapters demonstrate what works best and establishes optimized practices. It systematically examines well-researched principles of effective multimedia instruction and pinpoints exactly why certain practices succeed by isolating the boundary conditions. The volume is founded upon research findings in learning theory, giving it an informed perspective in explaining precisely

Learning and Teaching in Early Childhood: Pedagogies of Inquiry and Relationships is an introduction for early childhood educators beginning their studies. Reflecting the fact that there is no single correct approach to the challenges of teaching, this book explores teaching through two lenses: teaching as inquiry and teaching as relating. The first part of the book focuses on inquiry, covering early childhood learning environments, learning theories, play pedagogies, approaches to teaching and learning, documentation and assessment, and the policy, curriculum and regulatory requirements in Australia. The second part explores relationships in early childhood contexts and covers topics such as fostering meaningful and respectful relationships with children, and working with families, staff and the wider community. Written by well-respected academics in the field, Learning and Teaching in Early Childhood is a vital resource for those entering the early childhood education and care profes

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.

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 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

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