e-Learning by Design PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download e-Learning by Design PDF full book. Access full book title e-Learning by Design by William Horton. Download full books in PDF and EPUB format.
Author: William Horton
Publisher: John Wiley & Sons
ISBN: 1118047125
Category : Business & Economics
Languages : en
Pages : 639
Get Book Here
Book Description
From William Horton -- a world renowned expert with more than thirty-five years of hands-on experience creating networked-based educational systems -- comes the next-step resource for e-learning training professionals. Like his best-selling book Designing Web-Based Training, this book is a comprehensive resource that provides practical guidance for making the thousand and one decisions needed to design effective e-learning. e-Learning by Design includes a systematic, flexible, and rapid design process covering every phase of designing e-learning. Free of academic jargon and confusing theory, this down-to-earth, hands-on book is filled with hundreds of real-world examples and case studies from dozens of fields. "Like the book's predecessor (Designing Web-based Training), it deserves four stars and is a must read for anyone not selling an expensive solution. -- From Training Media Review, by Jon Aleckson, www.tmreview.com, 2007
Author: William Horton
Publisher: John Wiley & Sons
ISBN: 1118047125
Category : Business & Economics
Languages : en
Pages : 639
Get Book Here
Book Description
From William Horton -- a world renowned expert with more than thirty-five years of hands-on experience creating networked-based educational systems -- comes the next-step resource for e-learning training professionals. Like his best-selling book Designing Web-Based Training, this book is a comprehensive resource that provides practical guidance for making the thousand and one decisions needed to design effective e-learning. e-Learning by Design includes a systematic, flexible, and rapid design process covering every phase of designing e-learning. Free of academic jargon and confusing theory, this down-to-earth, hands-on book is filled with hundreds of real-world examples and case studies from dozens of fields. "Like the book's predecessor (Designing Web-based Training), it deserves four stars and is a must read for anyone not selling an expensive solution. -- From Training Media Review, by Jon Aleckson, www.tmreview.com, 2007
Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801
Get Book Here
Book Description
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Author: William Horton
Publisher:
ISBN:
Category : Computer-assisted instruction
Languages : en
Pages : 166
Get Book Here
Book Description
Does e-learning work? How much does e-learning benefit both the organization and its learners? Is e-learning a wise investment or a waste of corporate resources? Here's a book that answers these and other important questions and guides you in taking a systematic approach that will strengthen your case for e-learning into your organization. Use the book's interactive sections and visit the companion Website to download design forms, examples, job aids, and more!
Author: Douwe Osinga
Publisher: "O'Reilly Media, Inc."
ISBN: 1491995793
Category : Computers
Languages : en
Pages : 255
Get Book Here
Book Description
Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections
Author: Tim Slade
Publisher:
ISBN:
Category :
Languages : en
Pages : 234
Get Book Here
Book Description
LEARN HOW TO DESIGN ELEARNING THAT DELIVERS RESULTS (AND DOESN'T SUCK)! When you're tasked with creating your very first eLearning course, it can be hard (and downright scary) trying to figure out where to begin. You likely have a million questions running through your head. How do you plan your project and set it up for success? How do you collect learning content from your subject matter experts? Why do you need an eLearning storyboard, and how do you write one? How do you develop a prototype of your eLearning course? And, what the heck is a prototype anyway? When and where should you include interactivity? When should you let your SMEs review your course? And what's a "SME" again? And what do you do when they don't give you feedback? Trying to get answers to all of these questions can quickly leave you saying to yourself, "I don't think I'm cut out for this eLearning thing!" But, here's some good news: you're not alone! The truth is, most eLearning designers entered fell, stumbled, and dropped into the world of eLearning entirely by accident. This is where the second edition of The eLearning Designer's Handbook can help! In this book, Tim Slade will show you that the eLearning development process doesn't have to be as complicated as it might seem. If you're new to eLearning, Tim will walk you through the complete eLearning design and development process, providing you practical tips and advice, based on his own experience working as an eLearning designer for over a decade. With the second edition of The eLearning Designer's Handbook, you'll discover how to... Plan your eLearning project by conducting a kickoff meeting with your stakeholders. Conduct a needs analysis and recommend a training solution. Draft an eLearning project plan and development timeline. Define the learning objectives and create a blended training solution. Collect and organize your learning content into a design document. Create a course outline and draft a storyboard of your eLearning course. Create a prototype and develop your course with an eLearning authoring tool. Incorporate interactivity into your eLearning course. Reduce cognitive load and increase learning retention. Deliver and measure the effectiveness of your eLearning course. Conduct a retrospective at the end of your project. So, what's new in the second edition? With the second edition of The eLearning Designer's Handbook, Tim Slade went back to the drawing board to rewrite and redesign every single page of the book. Not only does the second edition include a boatload of new content on instructional design best practices, but it also includes even more practical content geared towards new eLearning designers. With the second edition of The eLearning Designer's Handbook, you'll get... 3X more content Full-color print Real-world examples More tools and templates
Author:
Publisher: iUniverse
ISBN: 0595276679
Category : Distance education
Languages : en
Pages : 162
Get Book Here
Book Description
Author: Nataša Brouwer
Publisher: Amsterdam University Press
ISBN: 9089646469
Category : Education
Languages : en
Pages : 129
Get Book Here
Book Description
Information and communication technology (ICT) makes it possible to bring information to everyone who wants to learn. Rapid advances in technology offer strong support for using ICT in teaching. Online education can intensify and improve students' learning process, and enables us to reach more students than by traditional means. The number of courses and modules being offered online is increasing rapidly worldwide. This is happening not only at traditional institutions for distance education, but even more so in the "classical" institutions for higher education. Such decisions are motivated by the improved technology available for web lectures, combined with financial motivations, i.e. being able to offer courses to larger groups of students. There is a huge trend toward open massive online courses or MOOCs. Prestigious universities offer MOOCs free of charge to a mass audience, often up to hundreds of thousands of learners. Although online education can reach more people nowadays and new and challenging learning experiences can be created with it, in the average university course the digital dimension too often remains limited to simply publishing the existing face-to-face course content online. Educational technology is often seen as an "extra", a luxury tool, and not as an integral and indispensable element of a university's teaching design. It is thus crucial that lecturers have and can obtain knowledge about how to design technology-enhanced teaching. Technical advances can be expected to continue in the future, and those who wish to implement educational technology in their own teaching practice must reckon on becoming lifelong learners. This fits the culture of academic teachers perfectly: they are already lifelong learners and creators of new knowledge within their discipline. This book is based on the notion that a lecturer who uses ICT in teaching must learn how to apply his or her knowledge about content, pedagogy and technology in an integrated manner. The idea of integrating these three types of knowledge is based on the TPACK model, which stands for Technological Pedagogical Content Knowledge model.
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Computers
Languages : en
Pages : 320
Get Book Here
Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Pam Anderson
Publisher: Clarkson Potter
ISBN: 0767902793
Category : Cooking
Languages : en
Pages : 306
Get Book Here
Book Description
Recalling an earlier era when cooks relied on sight, touch, and taste rather than cookbooks, the author encourages readers to rediscover the lost art of preparing food and use their imagination in the kitchen.
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
Get Book Here
Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
