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Создание новых игровых миров может стать вашей профессией! Индустрия разработки игр дает шанс раскрыть творческий потенциал, воплощая идеи в игровые проекты. А с чего вам следует начать, подскажет книга «Хочу в геймдев!», написанная ведущими специалистами игровой индустрии. Вы узнаете, в чем состоит работа гейм-дизайнера и других участников разработки, определите, какие навыки вам нужно оттачивать в первую очередь, познакомитесь с производственными процессами и разберетесь, как устроен мир геймдева. Если вы горите идеей делать игры, то эта книга – первый шаг на пути профессионального игродела! В формате PDF A4 сохранён издательский дизайн.

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You’re one step away from creating crystal-clear computer-aided drafts in AutoCAD Ever started an AutoCAD project, only to give up when you couldn’t quite get the hang of it? Or do you have a project coming up that would really benefit from a few meticulously created drawings? Then you need the latest edition of AutoCAD For Dummies , the world’s bestselling retail book about the wildly popular program. With coverage of all the important updates to AutoCAD released since 2019, this book walks you through the very basics of pixels, vectors, lines, text, and more, before moving on to more advanced step-by-step tutorials on three-dimensional drawings and models. Already know the fundamentals? Then skip right to the part you need! From blocks to parametrics, it’s all right here at your fingertips. You’ll also find: In-depth explanations of how to create and store your drawings on the web Stepwise instructions on creating your very first AutoCAD drawing, from product installation and project creation to the final touches An exploration of system variables you can tweak to get the best performance from AutoCAD Perfect for the AutoCAD newbie just trying to find their way around the interface for the first time, AutoCAD For Dummies is also a must-read reference for the experienced user looking to get acquainted with the program’s latest features and essential drawing tips. Grab a copy today!

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FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

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ADVANCED HEALTHCARE SYSTEMS This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists. The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound—that can be really helpful to rapid diagnosis. Audience This book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector.

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ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

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