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Designing Deep Learning Systems: A software engineer's guide
Designing Deep Learning Systems: A software engineer's guide
Designing Deep Learning Systems: A software engineer's guide
Характеристики та опис

Основні

ВиробникWAS

Користувальницькі характеристики

ISBN978-1633439863
АвторChi Wang, Donald Szeto
Рік2023
ВидавництвоManning
A vital guide to building the platforms and systems that bring deep learning models to production.SummaryIn Designing 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 platformDeep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these 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 technologyTo be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth.About the bookDesigning Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms.What's insideThe deep learning development cycleAutomate training in TensorFlow and PyTorchDataset management, model serving, and hyperparameter tuningA hands-on deep learning labAbout the readerFor software developers and engineering-minded data scientists. Examples in Java and Python.About the authorChi Wang is a principal software developer in the Salesforce Einstein group.Donald Szeto was the co-founder and CTO of PredictionIO.

Designing Deep Learning Systems: A software engineer's guide

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