Verfasst von: | Wang, Chi [VerfasserIn] |
| Szeto, Donald [VerfasserIn] |
Titel: | Designing deep learning systems |
Titelzusatz: | a guide for software engineers |
Verf.angabe: | Chi Wang and Donald Szeto ; code lab by Yan Xue ; foreword by Silvio Savarese and Caiming Xiong |
Verlagsort: | Shelter Island, NY |
Verlag: | Manning |
E-Jahr: | 2023 |
Jahr: | [2023] |
Umfang: | xx, 337 Seiten |
Illustrationen: | Illustrationen, Diagramme |
ISBN: | 978-1-63343-986-3 |
Abstract: | 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 |
| To 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. This book gives you that depth. Designing deep learning systems : a guide for software engineers 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 majot 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 |
URL: | Inhaltsverzeichnis: http://www.gbv.de/dms/bowker/toc/9781633439863.pdf |
| Cover: https://www.dietmardreier.de/annot/426F6F6B446174617C7C393738313633333433393836337C7C434F50.jpg?sq=1 |
Schlagwörter: | (s)Deep learning / (s)Software Engineering |
Dokumenttyp: | Einführung |
Sprache: | eng |
Sach-SW: | COM060180 |
| COMPUTERS / Programming / Software Development |
| Maschinelles Lernen |
| Software Engineering |
| Software Engineering |
| Web services |
| Webservices |
| Génie logiciel |
| Apprentissage profond |
| Deep learning (Machine learning) |
| Software engineering |
K10plus-PPN: | 1852844388 |
978-1-63343-986-3
Designing deep learning systems / Wang, Chi [VerfasserIn]; [2023]
69118045