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Taschenbuch. Zustand: Neu. Neural Networks for Electronics Hobbyists | A Non-Technical Project-Based Introduction | Richard Mckeon | Taschenbuch | xiv | Englisch | 2018 | Apress | EAN 9781484235065 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you'll build an electronics project, providing a hands-on experience in training a network.There are no prerequisites here and you won't see a single line of computer code in this book. Instead, ittakes a hardware approach using very simple electronic components.You'll start off with an interesting non-technical introduction to neural networks, and then construct an electronics project.The project isn't complicated, but it illustrates how back propagation can be used to adjust connection strengths or 'weights' and train a network.By the end of this book, you'll be able to take what you've learned and apply it to your own projects.If you like to tinker around with components and build circuits on a breadboard,Neural Networks for Electronics Hobbyists is the book for you.What You'll LearnGain a practical introduction to neural networksReview techniques for training networks with electrical hardware and supervised learningUnderstand how parallel processing differs from standard sequential programmingWho This Book Is ForAnyone interest in neural networks, from electronic hobbyists looking for an interesting project to build, to a layperson with no experience. Programmersfamiliar with neural networks but have only implemented them using computer code will also benefit from this book. 156 pp. Englisch.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Learn through a hardware-based approach to neural networks versus implementing a network in softwareNo technical background or programming skills are requiredThe in-book project is made from inexpensive, readily a.
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you'll build an electronics project, providing a hands-on experience in training a network.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 156 pp. Englisch.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you'll build an electronics project, providing a hands-on experience in training a network.There are no prerequisites here and you won't see a single line of computer code in this book. Instead, ittakes a hardware approach using very simple electronic components.You'll start off with an interesting non-technical introduction to neural networks, and then construct an electronics project.The project isn't complicated, but it illustrates how back propagation can be used to adjust connection strengths or 'weights' and train a network.By the end of this book, you'll be able to take what you've learned and apply it to your own projects.If you like to tinker around with components and build circuits on a breadboard,Neural Networks for Electronics Hobbyists is the book for you.What You'll LearnGain a practical introduction to neural networksReview techniques for training networks with electrical hardware and supervised learningUnderstand how parallel processing differs from standard sequential programmingWho This Book Is ForAnyone interest in neural networks, from electronic hobbyists looking for an interesting project to build, to a layperson with no experience. Programmersfamiliar with neural networks but have only implemented them using computer code will also benefit from this book.