Products related to Engineering:
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Higher Education : Progress for Management and Engineering
Increasing corporate social responsibility demands professionals possess the necessary knowledge, abilities, and competencies to answer the needs of a diverse organization’s stakeholders.This book highlights the most recent issues related to higher education in the fields of management and engineering.It explains why a sustainable education is a requirement for professionals, as well as the organizations they collaborate with. Higher Education: Progress for Management and Engineering focuses on the latest research findings in the field of higher and sustainable education.It discusses the progress, shares knowledge and insights on an international scale, and highlights the challenges faced to obtain and secure a more responsible and sustainable management system.Selecting different options and strategies, how to set priorities on managing competition, and how to succeed as an organization that can lead to successes in both national and international markets are covered within this book. This book can be used as a reference for researchers, academics, managers, engineers, and other professionals involved in higher and sustainable education in management and engineering.
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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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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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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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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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STEM in Early Childhood Education : How Science, Technology, Engineering, and Mathematics Strengthen Learning
Bringing together a diverse cohort of experts, STEM in Early Childhood Education explores the ways STEM can be integrated into early childhood curricula, highlighting recent research and innovations in the field, and implications for both practice and policy. Based on the argument that high-quality STEM education needs to start early, this book emphasizes that early childhood education must include science, technology, engineering, and mathematics in developmentally appropriate ways based on the latest research and theories.Experienced chapter authors address the theoretical underpinnings of teaching STEM in the early years, while contextualizing these ideas for the real world using illustrative examples from the classroom.This cutting-edge collection also looks beyond the classroom to how STEM learning can be facilitated in museums, nature-based learning outdoors, and after-school programs.STEM in Early Childhood Education is an excellent resource for aspiring and veteran educators alike, exploring the latest research, providing inspiration, and advancing best practices for teaching STEM in the early years.
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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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Engineering for Sustainable Development : Theory and Practice
ENGINEERING FOR SUSTAINABLE DEVELOPMENT AN AUTHORITATIVE AND COMPLETE GUIDE TO SUSTAINABLE DEVELOPMENT ENGINEERING In Engineering for Sustainable Development: Theory and Practice, a team of distinguished academics deliver a comprehensive, education-focused discussion on sustainable engineering, bridging the gap between theory and practice by drawing upon illuminating case studies and the latest cutting-edge research.In the book, readers will find an introduction to the sustainable development agenda and sustainable technology development, as well as practical methods and tools for the development and implementation of sustainable engineering solutions.The book highlights the critical role of engineers and the engineering profession in providing sustainability leadership as well as important future-focused solutions to support engineering global sustainable development.The book offers a wide range of civil, mechanical, electrical, and chemical engineering industry applications.Readers will also benefit from: A thorough introduction to contemporary sustainability challenges in the engineering discipline Comprehensive discussions of sustainability assessment tools, including triple bottom line assessment (TBL) and the environmental life cycle assessment (LCA) In-depth examinations of sustainable engineering strategies, including cleaner production and eco-efficiency methods and environmental management systems Detailed review of green engineering principles and industrial symbiosis in engineering application. A link between product stewardship and the design for the environment Perfect for graduate and senior undergraduate students in any engineering discipline, Engineering for Sustainable Development: Theory and Practice will also earn a place in the libraries of consultants and engineers in industry and government with a personal or professional interest in sustainability management.
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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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What is the focus in mechanical engineering, mechatronics, or product development?
In mechanical engineering, the focus is on designing, analyzing, and manufacturing mechanical systems and components such as engines, machines, and tools. Mechatronics combines mechanical engineering with electronics and computer science to develop intelligent systems and products, such as robotics and automation. Product development involves the entire process of creating a new product, from conceptualization and design to manufacturing and marketing, with a focus on meeting customer needs and market demands. Each field has its own unique focus, but all are centered around creating innovative and efficient solutions for various engineering challenges.
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Should I study civil engineering or industrial engineering?
The choice between civil engineering and industrial engineering ultimately depends on your interests and career goals. Civil engineering focuses on the design and construction of infrastructure projects such as buildings, bridges, and roads, while industrial engineering focuses on optimizing processes and systems in various industries. Consider your strengths, interests, and the type of work environment you prefer when making your decision. Researching the job market and potential career opportunities in each field can also help you make an informed decision.
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Should I study mechanical engineering or automotive engineering?
The decision between studying mechanical engineering or automotive engineering depends on your interests and career goals. If you are passionate about designing and developing automotive systems, such as engines, transmissions, and vehicle dynamics, then automotive engineering may be the right choice for you. On the other hand, if you are interested in a broader range of engineering principles and want to keep your career options open to various industries, then mechanical engineering may be a better fit. Consider your interests, strengths, and long-term career aspirations to make an informed decision.
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