Products related to Engineering:
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Teaching, Tutoring and Training in the Lifelong Learning Sector
This core text provides comprehensive support for pre-service and in-service trainee teachers in the Lifelong Learning Sector covering all they need to know to achieve QTLS status. Supporting trainees through all stages of their professional development, the text takes the reader through the theoretical background underpinning teaching and learning and offers practical guidance on day-to-day challenges. This fourth edition has been fully revised and updated and includes a new chapter on teaching practice with notes on observation and lesson planning.New information on behaviour management has been added to support trainees in an aspect of teaching that many find challenging.
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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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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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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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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.
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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.
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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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What is the difference between teaching and electrical engineering?
Teaching involves imparting knowledge and skills to students, often in a classroom setting, and requires strong communication and interpersonal skills. On the other hand, electrical engineering involves designing, developing, and testing electrical equipment and systems, and requires a strong understanding of mathematics and physics. While teaching focuses on educating and guiding students, electrical engineering focuses on applying scientific principles to create and improve electrical systems and devices. Both fields require critical thinking and problem-solving skills, but the specific technical knowledge and focus areas differ.
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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
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.
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Computer Science Education : Perspectives on Teaching and Learning in School
Drawing together the most up-to-date research from experts all across the world, the second edition of Computer Science Education offers the most up-to-date coverage available on this developing subject, ideal for building confidence of new pre-service and in-service educators teaching a new discipline.It provides an international overview of key concepts, pedagogical approaches and assessment practices. Highlights of the second edition include:- New sections on machine learning and data-driven (epistemic) programming- A new focus on equity and inclusion in computer science education- Chapters updated throughout, including a revised chapter on relating ethical and societal aspects to knowledge-rich aspects of computer science education- A new set of chapters on the learning of programming, including design, pedagogy and misconceptions- A chapter on the way we use language in the computer science classroom. The book is structured to support the reader with chapter outlines, synopses and key points.Explanations of key concepts, real-life examples and reflective points keep the theory grounded in classroom practice. The book is accompanied by a companion website, including online summaries for each chapter, 3-minute video summaries by each author and an archived chapter on taxonomies and competencies from the first edition.
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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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What is chemical engineering process engineering?
Chemical engineering process engineering is a branch of engineering that focuses on designing, optimizing, and operating processes that involve chemical or biological transformations to produce valuable products. It involves the application of principles from chemistry, physics, biology, and mathematics to develop efficient and sustainable processes in industries such as pharmaceuticals, food and beverage, energy, and environmental protection. Chemical engineers in process engineering work on various stages of a process, from conceptualization and design to implementation and troubleshooting, to ensure the safe and efficient production of desired products.
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What is process engineering chemical engineering?
Process engineering in chemical engineering involves designing, optimizing, and operating processes that involve the transformation of raw materials into valuable products. It focuses on the development and implementation of efficient and sustainable processes in industries such as pharmaceuticals, food and beverage, and petrochemicals. Process engineers use principles of chemistry, physics, and mathematics to improve the efficiency, safety, and cost-effectiveness of chemical processes. They play a crucial role in ensuring that chemical processes are environmentally friendly and meet regulatory standards.
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Which book would you recommend for learning electrical engineering?
I would recommend "Fundamentals of Electric Circuits" by Charles K. Alexander and Matthew N.O. Sadiku for learning electrical engineering. This book covers the basic principles of electric circuits in a clear and comprehensive manner, making it suitable for beginners. It also includes numerous examples and practice problems to help reinforce understanding of the material. Overall, it is a highly regarded textbook in the field of electrical engineering.
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What is control engineering in electrical engineering?
Control engineering in electrical engineering is the application of control theory to design systems that regulate the behavior of dynamic systems. It involves the use of mathematical models and algorithms to manipulate the input to a system in order to achieve the desired output. Control engineers design and implement control systems for a wide range of applications, such as robotics, power systems, and industrial automation. This field is crucial for ensuring the stability, efficiency, and safety of electrical systems.
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