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Products related to Engineering:


  • 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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  • 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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  • 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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  • Are you overwhelmed with teaching electrical engineering?

    As an AI, I do not experience emotions or feelings, so I do not get overwhelmed with teaching electrical engineering. My purpose is to assist and provide information to the best of my abilities. If you are feeling overwhelmed with teaching electrical engineering, it may be helpful to break down the material into smaller, more manageable sections, seek support from colleagues or mentors, and prioritize self-care to prevent burnout.

  • 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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  • Philosophy and Engineering: An Emerging Agenda
    Philosophy and Engineering: An Emerging Agenda

    Whereas science, technology, and medicine have all called forth dedicated philosophical investigations, a fourth major contributor to the technoscientific world in which we all live - that is, engineering - has been accorded almost none of the philosophical attention it deserves.This volume thus offers a first characterisation of this important new field, by some of the primary philosophers and ethicists interested in engineering and leading engineers interested in philosophical reflections.The volume deals with such questions as: What is engineering?In what respect does engineering differ from science?What ethical problems does engineering raise? By what ethical principles are engineers guided? How do engineers themselves conceive of their profession?What do they see as the main philosophical challenges confronting them in the 21st century?The authors respond to these and other questions from philosophical and engineering view points and so illustrate how together they can meet the challenges and realize the opportunities present in the necessary encounters between philosophy and engineering - encounters that are ever more important in an increasingly engineered world and its problematic futures.

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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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  • Mathematical Engineering of Deep Learning
    Mathematical Engineering of Deep Learning

    Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics.The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning.These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks.Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade.The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains.The focus is on the basic mathematical description of algorithms and methods and does not require computer programming.The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research.The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning. Key Features:A perfect summary of deep learning not tied to any computer language, or computational framework. An ideal handbook of deep learning for readers that feel comfortable with mathematical notation. An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains. The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials. Deep learning is easily described through the language of mathematics at a level accessible to many professionals.Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.

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  • Senior Design Projects in Mechanical Engineering : A Guide Book for Teaching and Learning
    Senior Design Projects in Mechanical Engineering : A Guide Book for Teaching and Learning

    This book offers invaluable insights about the full spectrum of core design course contents systematically and in detail.This book is for instructors and students who are involved in teaching and learning of ‘capstone senior design projects’ in mechanical engineering. It consists of 17 chapters, over 300 illustrations with many real-world student project examples. The main project processes are grouped into three phases, i.e., project scoping and specification, conceptual design, and detail design, and each has dedicated two chapters of process description and report content prescription, respectively.The basic principles and engineering process flow are well applicable for professional development of mechanical design engineers. CAD/CAM/CAE technologies are commonly used within many project examples.Thematic chapters also cover student teamwork organization and evaluation, project management, design standards and regulations, and rubrics of course activity grading.Key criteria of successful course accreditation and graduation attributes are discussed in details.In summary, it is a handy textbook for the capstone design project course in mechanical engineering and an insightful teaching guidebook for engineering design instructors.

    Price: 49.99 £ | Shipping*: 0.00 £
  • 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 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.

  • Can one study teaching as a Master of Engineering?

    While it is not common to study teaching as a Master of Engineering, some universities may offer specialized programs that combine engineering with education or teaching. These programs may focus on preparing engineers to become educators or teaching engineering concepts to others. However, individuals interested in pursuing a career in teaching may be better suited to a Master of Education program or a teaching certification program.

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