Download or read book Learning Engineering Toolkit written by Jim Goodell and published by Taylor & Francis. This book was released on 2022-07-25 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Learning Engineering Toolkit is a practical guide to the rich and varied applications of learning engineering, a rigorous and fast-emerging discipline that synthesizes the learning sciences, instructional design, engineering design, and other methodologies to support learners. As learning engineering becomes an increasingly formalized discipline and practice, new insights and tools are needed to help education, training, design, and data analytics professionals iteratively develop, test, and improve complex systems for engaging and effective learning. Written in a colloquial style and full of collaborative, actionable strategies, this book explores the essential foundations, approaches, and real-world challenges inherent to ensuring participatory, data-driven, learning experiences across populations and contexts.

Download or read book Engineering Effective Learning Toolkit written by Harold D. Stolovitch and published by Pfeiffer. This book was released on 2003-08-05 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engineering Effective Learning Engineering Effective Learning Toolkit offers a systematic, step-by-step approach for designing, managing, and evaluating successful training, learning, and performance projects. Harold D. Stolovitch and Erica J. Keeps–international leaders in the field of workplace learning and performance and coeditors of both editions of the Handbook of Human Performance Technology–have designed this toolkit based on their popular course that has been conducted and tested with hundreds of leading organizations worldwide. A hands-on resource, Engineering Effective Learning Toolkit is filled with illustrative, real-world examples and includes on CD-ROM easily reproducible and customizable information charts and job aids to help you accomplish each step in the instructional design process. This indispensable toolkit is a personal "coach" you can refer to on an "as-needed" basis or use to complete a training project from start to finish. Engineering Effective Learning Toolkit is the first book in the Learning and Performance Toolkit Series. Praise for Engineering Effective Learning Toolkit "Watching great instructional design is like observing great ice skating. The audience is wowed by the flow and beauty and usually has no inkling about all it took to get there. Erica and Harold know how to make the magic happen. They also know how to make it easy for their readers. They offer us all the ingredients for their special choreography." –Beverly Kaye, CEO and founder, Career Systems International and author, Up Is Not The Only Way; coauthor, Love ‘Em or Lose ‘Em: Getting Good People to Stay "All instructional designers with varying degrees of experience interested in improving performance through systematically designed training programs can benefit from the Toolkit. This book provides an organization with a solid foundation to make significant performance improvements quickly. We know because we use Harold’s and Erica’s process and tools with great success." –Michel Roy, human resources manager, Alcan Primary Metal "This book, written by two of the world’s most experienced instructional designers, is filled with ready-to-use and proven-to-work job aids, tools, checklists and charts. If you develop learning programs of any type, this is the book to use!" –Dana and Jim Robinson, principals, Partners in Change and coauthors, Performance Consulting and Zap the Gaps! "As a training manager for the last 20 years, I have used and implemented the Stolovitch and Keeps Engineering Effective Learning process. It has proven to be very efficient in designing, developing, and implementing instructional interventions. I am thrilled to see that this process will now be available to all!" –Daniel Dupont, chief learning officer, Société des Alcools du Québec "In a very clear and easily executable fashion, the authors have managed to provide a roadmap for success. It is as applicable for seasoned veterans as it is for individuals new to the instructional design process." –Lisa Cavallaro, manager, learning & development, WW Talent Resourcing & Development, Cisco Systems "Clear, concise, practical, proven, and useful–this provides all one has to know to design successful learning." –Roger Kaufman, professor, office for needs assessment & planning, Florida State University, and director, Roger Kaufman & Associates "If you’re involved with the design of learning, then this book has something to offer you. From beginner to the most advanced instructional designer, there are tools and tips that you can immediately and effectively put to use!" –Frank S. Wilmoth, Director for Learning Excellence, Prudential Real Estate and Relocation Services

Download or read book Learn Social Engineering written by Dr. Erdal Ozkaya and published by Packt Publishing Ltd. This book was released on 2018-04-30 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improve information security by learning Social Engineering. Key Features Learn to implement information security using social engineering Get hands-on experience of using different tools such as Kali Linux, the Social Engineering toolkit and so on Practical approach towards learning social engineering, for IT security Book Description This book will provide you with a holistic understanding of social engineering. It will help you to avoid and combat social engineering attacks by giving you a detailed insight into how a social engineer operates. Learn Social Engineering starts by giving you a grounding in the different types of social engineering attacks,and the damages they cause. It then sets up the lab environment to use different toolS and then perform social engineering steps such as information gathering. The book covers topics from baiting, phishing, and spear phishing, to pretexting and scareware. By the end of the book, you will be in a position to protect yourself and your systems from social engineering threats and attacks. All in all, the book covers social engineering from A to Z , along with excerpts from many world wide known security experts. What you will learn Learn to implement information security using social engineering Learn social engineering for IT security Understand the role of social media in social engineering Get acquainted with Practical Human hacking skills Learn to think like a social engineer Learn to beat a social engineer Who this book is for This book targets security professionals, security analysts, penetration testers, or any stakeholder working with information security who wants to learn how to use social engineering techniques. Prior knowledge of Kali Linux is an added advantage

Download or read book Design Recommendations for Intelligent Tutoring Systems Volume 11 Professional Career Education written by Anne Sinatra and published by U.S. Army Combat Capabilities Development Command – Soldier Center. This book was released on 2023-09-01 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Design Recommendations for Intelligent Tutoring Systems series has covered many different topics over the past ten years. Those topics have ranged from general components of intelligent tutoring systems (ITSs) (Learner Modeling, Instructional Management, Authoring Tools, Domain Modeling) to advanced elements (Assessment Methods, Team Tutoring, Self-Improving Systems, Data Visualization, Competency Based-Scenario Design). Our most recent previous volume included a series of Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analyses on all the initial topics as well as overviews of ITSs in general and the Generalized Intelligent Framework for Tutoring (GIFT) software (Sottilare et al., 2012; Sottilare et al., 2017; Goldberg & Sinatra, 2023). Each book in the Design Recommendations for Intelligent Tutoring Systems series has been associated with an Expert Workshop on the same topic. These workshops are part of a cooperative agreement (W911NF18-2-0039) between US Army Combat Capabilities Development Command (DEVCOM) Soldier Center and University of Memphis. One of the goals of the expert workshops is to learn more about ITS capabilities that are being developed, and how these approaches, as well as lessons learned, could enhance the GIFT software (GIFT is freely available at https://www.GIFTtutoring.org). Invited experts in industry, academia, and government discuss the expert workshop topic, their applicable work, and suggestions for improving GIFT in what is usually a two day event. Both the University of Memphis and GIFT Teams participate in the workshop, help to guide discussion, and ask questions that will provide insight into current challenges in GIFT. The expert workshop associated with this current book was held virtually in October 2022, and included presentations about both general approaches and specific applications to professional education in ITSs. Additionally, the University of Memphis team that participated in the workshop included Arthur C. Graesser, Xiangen Hu, Vasile Rus, and Jody Cockroft. The US Army DEVCOM Soldier Center team who participated in the workshop included Benjamin Goldberg, Gregory Goodwin, Anne M. Sinatra, Randall Spain, and Lisa N. Townsend. The current volume and the expert workshop that was associated with it, branched out in a new direction and rather than addressing specific components of an ITS or types of features/approaches that could be included in ITSs, it focused on how to apply an ITS for specific types of training. The specific focus was on ITSs for Professional Career Education. This topic area was selected, as in general, ITS research tends to be focused on K-12 or college education, and in many cases on domains such as algebra or physics. However, for the military, and for industry, trainees are adult learners and domains tend to be more active, applied, and experiential. This workshop provided an opportunity for discussion of specific examples of applied training that occurs with ITSs, as well as discussion of general approaches and considerations for applied professional education in ITSs.

Download or read book Machine Learning Engineering in Action written by Ben Wilson and published by Simon and Schuster. This book was released on 2022-05-17 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.

Download or read book WordPerfect 6 0 for Engineers written by Sheryl Sorby and published by Prentice Hall. This book was released on 1995 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now you can design a learning package that fits your introductory engineering course perfectly-with The Engineer's Toolkit: A First Course in Engineering. The Engineer's Toolkit is Prentice Hall's innovative publishing program for introductory engineering. Consisting of modules that cover engineering skills and concepts, programming languages and software tools, The Engineer's Toolkit is a flexible solution for keeping up with the evolving curriculum of first-year engineering.

Download or read book Educational Data Science written by Alejandro Peña-Ayala and published by Springer Nature. This book was released on 2023 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well-sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge about learning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning!

Download or read book Technology and Tools in Engineering Education written by Prathamesh P. Churi and published by CRC Press. This book was released on 2021-10-28 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the innovative and research methods of the teaching-learning process in Engineering field. It focuses on the use of technology in the field of education. It also provides a platform to academicians and educationalists to share their ideas and best practices. The book includes specific pedagogy used in engineering education. It offers case studies and classroom practices which also include those used in distance mode and during the COVID-19 pandemic. It provides comparisons of national and international accreditation bodies, directions on cost-effective technology, and it discusses advanced technologies such as VR and augmented reality used in education. This book is intended for research scholars who are pursuing their masters and doctoral studies in the engineering education field as well as teachers who teach undergraduate and postgraduate courses to engineering students.

Download or read book Fundamentals and Frontiers of Medical Education and Decision Making written by Jordan Richard Scheonherr and published by Taylor & Francis. This book was released on 2024-07-22 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals and Frontiers of Medical Education and Decision-Making brings together international experts to consider the theoretical, practical, and sociocultural foundations of health professions education. In this volume, the authors review the foundational theories that have informed the early transition to competency-based education. Moving beyond these monolithic models, the authors draw from learning and psychological sciences to provide a means to operationalize competencies. The chapters cover fundamental topics including the transition from novices to experts, the development of psychomotor skills in surgery, the role of emotion and metacognition in decision-making, and how practitioners and laypeople represent and communicate health information. Each section provides chapters that integrate and advance our understanding of health professions education and decision- making. Grounded in psychological science, this book highlights the fundamental issues faced by healthcare professionals, and the frontiers of learning and decision-making. It is important reading for a wide audience of healthcare professionals, healthcare administrators, as well as researchers in judgment and decision-making.

Download or read book Machine Learning Engineering with Python written by Andrew P. McMahon and published by Packt Publishing Ltd. This book was released on 2023-08-31 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain Key Features This second edition delves deeper into key machine learning topics, CI/CD, and system design Explore core MLOps practices, such as model management and performance monitoring Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools Book DescriptionThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.What you will learn Plan and manage end-to-end ML development projects Explore deep learning, LLMs, and LLMOps to leverage generative AI Use Python to package your ML tools and scale up your solutions Get to grips with Apache Spark, Kubernetes, and Ray Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow Detect drift and build retraining mechanisms into your solutions Improve error handling with control flows and vulnerability scanning Host and build ML microservices and batch processes running on AWS Who this book is for This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.

Download or read book Product Engineering written by Doru Talaba and published by Springer Science & Business Media. This book was released on 2008-04-06 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains an edited version of the lectures and selected contributions presented during the Advanced Summer Institute (ASI) on "Product Engineering: Tools and Methods based on Virtual Reality" held at Chania (Greece), 30th May - 6th June 2007. The ASI was devoted to the Product Engineering field, with particular attention being given to the aspects related to Virtual Reality (VR) technologies, and their use and added value in engineering.

Download or read book Concurrent Engineering written by Andrew Kusiak and published by John Wiley & Sons. This book was released on 1993-01-12 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a top-down approach to the design, development, testing and recyclability of products, components and systems across a wide range of industries. Starting with the desired result and working back through the details, it shows how to produce goods, taking into account the challenges of actual manufacture, what the reliability requirements should be, quality control, associated costs, customer needs and more. Additional features include case studies and team negotiating. Also well-illustrated with figures, photographs, charts and tables and includes an extensive bibliography.

Download or read book Engineering Effective Learning Toolkit written by D.M. Bartels and published by . This book was released on 2003-07-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Instructional Systems Design and Evaluation written by Robert A. Sottilare and published by Springer Nature. This book was released on 2021-07-03 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 12792 and 12793 constitutes the refereed proceedings of the Third International Conference on Adaptive Instructional Systems, AIS 2021, held as Part of the 23rd International Conference, HCI International 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The papers of AIS 2021, Part I, are organized in topical sections named: Conceptual Models and Instructional Approaches for AIS; Designing and Developing AIS; Evaluation of AIS; Adaptation Strategies and Methods in AIS. Chapter “Personalized Mastery Learning Ecosystems: Using Bloom’s Four Objects of Change to Drive Learning in Adaptive Instructional Systems” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Download or read book HCI International 2022 Late Breaking Posters written by Constantine Stephanidis and published by Springer Nature. This book was released on 2022-11-23 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume CCIS 1655 is part of the refereed proceedings of the 24th International Conference on Human-Computer Interaction, HCII 2022, which was held virtually during June 26 to July 1, 2022. A total of 5583 individuals from academia, research institutes, industry, and governmental agencies from 88 countries submitted contributions, and 1276 papers and 275 posters were included in the proceedings that were published just before the start of the conference. Additionally, 296 papers and 181 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work” (papers and posters). The contributions thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas.

Download or read book Machine Learning Engineering in Action written by Ben Wilson and published by Simon and Schuster. This book was released on 2022-04-26 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you’ll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You’ll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You’ll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author’s extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it’s so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

Download or read book Knowledge Engineering Tools and Techniques for AI Planning written by Mauro Vallati and published by Springer Nature. This book was released on 2020-03-25 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive review for Knowledge Engineering tools and techniques that can be used in Artificial Intelligence Planning and Scheduling. KE tools can be used to aid in the acquisition of knowledge and in the construction of domain models, which this book will illustrate. AI planning engines require a domain model which captures knowledge about how a particular domain works - e.g. the objects it contains and the available actions that can be used. However, encoding a planning domain model is not a straightforward task - a domain expert may be needed for their insight into the domain but this information must then be encoded in a suitable representation language. The development of such domain models is both time-consuming and error-prone. Due to these challenges, researchers have developed a number of automated tools and techniques to aid in the capture and representation of knowledge. This book targets researchers and professionals working in knowledge engineering, artificial intelligence and software engineering. Advanced-level students studying AI will also be interested in this book.