learning javascript

This book enables readers to quickly develop a working knowledge of HTML, JavaScript and PHP. The text emphasizes a hands-on approach to learning and makes extensive use of examples. A detailed scienc

This book will teach you how to take advantage of the JavaScript language to process data provided on the Internet. Much attention is given to the main JavaScript backbone: prototype based objects, a

《寶寶的JavaScript》為Java語言程序設計兒童讀物，學標準化語言，無論多早都不早！給寶寶演示如何在網頁中用JavaScript添加一個功能，並讓寶寶熟悉如何用函數、變量和事件來搭建一個網頁。

A Beginner's Guide to Computer Programming Start Programming Using HTML, CSS, and JavaScript is a manual for undergraduate students in engineering and the natural sciences to discover how computer pro

Learn how to program JavaScript while creating interactive audio applications with JavaScript for Sound Artists: Learn to Code With the Web Audio API! William Turner and Steve Leonard showcase the bas

A two-book set for web designers and front-end developersThis two-book set combines the titles HTML & CSS: Designing and Building Web Sites and JavaScript & jQuery: Interactive Front-End Devel

Ready to learn how to code a game? Get an introduction to programming with this fun and accessible guide. Learn HTML and JavaScript. Design and build five interactive computer games. Create cool graph

Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, C

E-Learning or electronic learning refers to the practice of teaching and learning using digital technology and multimedia. E-Learning technologies use flash and PowerPoint software to develop interact

This book focuses on developing methods for constructing learning paths in terms of “learning resources” (learning contents), “learning approaches” (learning method), and “learning quality” (learning

During the past twenty-five years, researchers have made impressive advances in pinpointing effective learning strategies (namely, activities the learner engages in during learning that are intended to improve learning). In Learning as a Generative Activity: Eight Learning Strategies that Promote Understanding, Logan Fiorella and Richard E. Mayer share eight evidence-based learning strategies that promote understanding: summarizing, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. Each chapter describes and exemplifies a learning strategy, examines the underlying cognitive theory, evaluates strategy effectiveness by analyzing the latest research, pinpoints boundary conditions, and explores practical implications and future directions. Each learning strategy targets generative learning, in which learners actively make sense out of the material so they can apply their learning to new situations. This concise, accessible introduction to learning strategi

Interactive Student Centered Learning: A Cooperative Approach to Learning concentrates on Student Centered Learning (SCL) which encompasses Active Learning (AL), Cooperative Learning (COL), Collaborat

Bernice Wong's Learning about Learning Disabilities was the first text to give equal attention to the intellectual, conceptual, and practical aspects of learning disabilities. The Third Edition of thi

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

Data-Driven Learning (DDL) can be broadly defined as the use of corpus tools and techniques for learners and teachers of foreign or second language, typically in the form of concordances derived from authentic texts for inductive learning of lexicogrammar. This Element is a practical guide for language teachers and graduate students intending to explore or upgrade their use of corpora in the language classroom and beyond. In today's context, where advances in computing and information processing dominate our social and professional interactions, the use of corpora emerges as a prime resource with which to approach data-driven language learning and teaching, developing language awareness, noticing skills and critical thinking for learning that generative AI cannot do for you.

This enthusiastic introduction to the fundamentals of information theory builds from classical Shannon theory through to modern applications in statistical learning. Includes over 210 student exercises, emphasising practical applications in statistics, machine learning and modern communication theory. Accompanied by online instructor solutions.

This book explores teaching, learning, and leadership in higher education following the Covid-19 pandemic. It examines opportunities that currently exist within higher education as they relate to innovative teaching and learning strategies, from instructional modalities to new models of transformative learning to meet students "where they are" in terms of career development and lifelong learning. Emphasis is placed on educational leadership and management skills, faculty and teaching acumen, and students and their quest for knowledge and understanding as we navigate past a global health crisis towards a future of hope and solutions to some of today's most pressing issues using collaboration, community, and an inquiry-oriented approach. The current state of education is reimagined with emphasis on higher education as a learning organization. A sense of urgency in higher education is underscored to instill knowledge and competency, encourage innovation, and help the next generation of st

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

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.

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

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

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

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

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.

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

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

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.

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

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.