9781633439863 - engineering deep learning systems: a guide for software engineers de wang, chi; szeto, donald (25 resultados)
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Paperback. Condición: New. 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 learnin…g 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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Paperback. Condición: new. Paperback. 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 excitingand lucrativecareer 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condición: New. Über den AutorChi Wang is a principal software developer in the Salesforce Einstein group where he builds the deep learning platform for millions of Salesforce customers. Previously, he worked at Microsoft Bing and Azure on b.
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Paperback. Condición: New. 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 learnin…g 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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Taschenbuch. Condición: Neu. Neuware - A vital guide to building the platforms and systems that bring deep learning models to production.In Designing Deep Learning Systems you will learn how to.
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Paperback. Condición: new. Paperback. 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 excitingand lucrativecareer 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
