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  • International Handbook of Engineering Education Research
    International Handbook of Engineering Education Research

    Winner of the 2024 American Educational Research Association (AERA) Division I Outstanding Research Publication AwardThis comprehensive handbook offers a broad overview of contemporary research on engineering education and its practical application.Over the past two decades, the field of engineering education research (EER) has become a vibrant and impactful community with new journals, conferences, and doctoral and research programs established across the globe.The increased interest in this area has helped improve the education and training of the next generation of engineers, as well as supporting growth in the use of technology for teaching and learning, increased attention to broadening participation, diversity and inclusion in the field, and a wide international expansion of the field. Drawing on the work of 100 expert contributors from over 20 countries, this volume covers both emergent and established areas of research within engineering education, giving voice to newcomers to the field as well as perspectives from established experts.Contents include:Sociocognitive and affective perspectives on engineering education. Technology and online learning in engineering education. Cultural and ethical issues including diversity, equity, and inclusion in engineering education. Curriculum design, teaching practices, and teacher education at all levels. Research methods and assessment in engineering education. This book offers an innovative and in-depth overview of engineering education scholarship and practice, which will be of use to researchers in engineering education, engineering educators and faculty, teacher educators in engineering education or STEM education, and other engineering and STEM-related professional organizations. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.

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  • General Engineering Knowledge
    General Engineering Knowledge

    This book covers the general engineering knowledge required by candidates for the Department of Transport's Certificates of Competency in Marine Engineering, Class One and Class Two.The text is updated throughout in this third edition, and new chapters have been added on production of fresh water and on noise and vibration.Reference is also provided to up-to-date papers and official publications on specialized topics.These updates ensure that this little volume will continue to be a useful pre-examination and revision text. - Marine Engineers Review, January 1992

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  • Knowledge Engineering for Modern Information Systems : Methods, Models and Tools
    Knowledge Engineering for Modern Information Systems : Methods, Models and Tools

    Knowledge Engineering (KE) is a field within artificial intelligence that develops knowledgebased systems.KE is the process of imitating how a human expert in a specific domain would act and take decisions.It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes.Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached.It is having all the rules and reasoning mechanisms to provide solutions to real-world problems.This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain.Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics.In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.

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  • Machine Learning Engineering
    Machine Learning Engineering

    From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders."If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book."-Cassie Kozyrkov, Chief Decision Scientist at Google"Foundational work about the reality of building machine learning models in production."-Karolis Urbonas, Head of Machine Learning and Science at Amazon

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  • What is the difference between Information Systems Engineering and Engineering Informatics?

    Information Systems Engineering focuses on designing, developing, and managing information systems that support business processes and decision-making. It involves the integration of technology, people, and processes to create efficient and effective systems. On the other hand, Engineering Informatics focuses on the application of computational tools and techniques to engineering problems. It involves the use of data analytics, simulation, and modeling to optimize engineering processes and systems. In summary, Information Systems Engineering is more focused on information systems for business purposes, while Engineering Informatics is more focused on applying computational tools to engineering problems.

  • How much prior knowledge is required for studying automotive engineering and mechanical engineering?

    Prior knowledge required for studying automotive engineering and mechanical engineering varies depending on the specific program and institution. Generally, a strong foundation in mathematics, physics, and chemistry is essential for both fields. Some programs may also require prior coursework in engineering principles and design. However, many universities offer introductory courses to help students without a background in engineering catch up and succeed in their studies. Overall, a passion for problem-solving, critical thinking, and a willingness to learn are key qualities for success in these fields.

  • How much prior knowledge is required for studying vehicle engineering and mechanical engineering?

    Prior knowledge required for studying vehicle engineering and mechanical engineering varies depending on the specific program and institution. Generally, a strong foundation in mathematics, physics, and chemistry is essential for both fields. Additionally, having a basic understanding of mechanical systems and engineering principles can be beneficial. However, many programs offer introductory courses to help students without prior knowledge catch up and succeed in their studies. Ultimately, a passion for the subject and a willingness to learn are key factors in being successful in these fields.

  • Is prior knowledge important for studying engineering?

    Yes, prior knowledge is important for studying engineering. Having a strong foundation in math, science, and problem-solving skills can help students grasp the complex concepts and theories in engineering. Additionally, prior knowledge can also help students make connections between different engineering principles and apply them to real-world problems. Overall, having a solid understanding of fundamental concepts can greatly benefit students in their engineering studies.

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  • Knowledge Engineering : The Process Paradigm
    Knowledge Engineering : The Process Paradigm

    Knowledge management is far-reaching. It can dramatically reduce costs such as costs of office work repetition, human resource retirement, information reuse, etc.Rather than "reinventing the wheel" and having it be a costly and inefficient activity, systematic reuse of knowledge can show substantial cost benefits immediately.This book shows how to develop process-oriented methodologies, covers both interorganizational and enterprises models, discusses how knowledge management can dramatically reduce costs and increase speed of response, presents a wide range of quantitative methods applied to various knowledge engineering problems, and offers several graphical presentations of models and processes.Academicians and practitioners in the area of knowledge management and engineering, especially managers in industries will fine this book useful.The material might also be useful in knowledge management graduate studies.

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  • Engineering Deep Learning Systems
    Engineering Deep Learning Systems

    Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models. In Engineering Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systemsRecognize and solve common engineering challenges for deep learning systemsUnderstand the deep learning development cycleAutomate training for models in TensorFlow and PyTorchOptimize dataset management, training, model serving and hyperparameter tuningPick the right open-source project for your platformEngineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning.It's full of hands-on examples that will help you transfer your software development skills to implementing deep learning platforms.You'll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning.This book is the perfect way to step into an exciting-and lucrative-career as a deep learning engineer. about the technology Behind every deep learning researcher is a team of engineers bringing their models to production.To build these systems, you need to understand how a deep learning system's platform differs from other distributed systems.By mastering the core ideas in this book, you'll be able to support deep learning systems in a way that's fast, repeatable, and reliable.

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  • Reference Shelf: Gene Editing & Genetic Engineering
    Reference Shelf: Gene Editing & Genetic Engineering

    The recent research into using the CRISPR gene-editing technology to change genetic coding in an organism’s DNA has generated tremendous public interest, but where is this technology going?In this issue of The Reference Shelf we look at gene editing and genetic engineering, drawing from periodicals, medical reports, and politics to see how this debate is progressing within popular culture.Issues investigated will include engineering new organisms, the potential for medical breakthroughs, the ethics of altering the human genome, and the campaign to bring back the dinosaurs.

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  • Machine Learning Engineering in Action
    Machine Learning Engineering in Action

    Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems.You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!You will learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience.You will even discover when not to use machine learning—and the alternative approaches that might be cheaper and more effective.When you're done working through this toolbox guide, you will be able to reliably deliver cost-effective solutions for organizations big and small alike. Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul.By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt.

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  • What education before studying mechanical engineering?

    Before studying mechanical engineering, individuals typically pursue a strong foundation in mathematics and physics, as these subjects are fundamental to understanding the principles of engineering. Additionally, coursework in chemistry, computer science, and materials science can also be beneficial. Hands-on experience through internships, projects, or extracurricular activities related to engineering can provide valuable practical skills and insights into the field. Overall, a well-rounded education that includes a mix of theoretical knowledge and practical experience can help prepare individuals for success in studying mechanical engineering.

  • What is the difference between electrical engineering, electronics, information technology, and computer engineering?

    Electrical engineering focuses on the generation, distribution, and control of electrical energy, as well as the design of electrical systems. Electronics deals with the study of electronic components, circuits, and systems that use electricity to process information. Information technology involves the use of computers and software to manage and process data. Computer engineering combines elements of both electrical engineering and computer science to design and develop computer systems and hardware. Each field has its own specialized focus and applications within the broader realm of technology.

  • What mathematical knowledge is required for electrical engineering?

    Electrical engineering requires a strong foundation in mathematics, including calculus, differential equations, linear algebra, and complex numbers. These mathematical concepts are essential for understanding and analyzing electrical circuits, electromagnetic fields, signal processing, and control systems. Additionally, knowledge of probability and statistics is important for analyzing and designing electrical systems. Overall, a solid understanding of mathematical principles is crucial for success in electrical engineering.

  • Is prior knowledge essential for studying vehicle engineering?

    Prior knowledge is not essential for studying vehicle engineering, as many programs are designed to teach students the necessary skills and knowledge from the ground up. However, having a strong foundation in math and physics can be beneficial for understanding the principles of vehicle engineering. Additionally, a passion for automobiles and a curiosity about how they work can also be helpful in pursuing a career in this field. Ultimately, while prior knowledge can be advantageous, it is not a requirement for studying vehicle engineering.

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