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.

N� de ref. del art�culo LU-9781633439863

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Detalles bibliogr�ficos

T�tulo: Engineering Deep Learning Systems
Editor: Manning Publications, US
Fecha de publicaci�n: 2023
Idioma: Ingl�s
ISBN 10: 1633439860
ISBN 13: 9781633439863
Encuadernaci�n: Paperback
Estado: New
Dimensiones: 7,38�pulgadas de ancho por 0,9 de alto por 9,25 de largo
Peso: 660 gramos

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Sinopsis

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 systems
Recognize and solve common engineering challenges for deep learning systems
Understand the deep learning development cycle
Automate training for models in TensorFlow and PyTorch
Optimize dataset management, training, model serving and hyperparameter tuning
Pick the right open-source project for your platform

Engineering 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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Chi 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 building large-scale distributed systems. Chi has filed six patents, mostly in deep learning systems.

Donald Szeto was the co-founder and CTO of PredictionIO, a startup that aimed to help democratize and accelerate the adoption of machine learning. PredictionIO was acquired by Salesforce, where he continued his work on machine learning and deep learning systems. Donald is currently investing in, advising, and mentoring technology startups.

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