Designing Deep Learning Systems – A software engineer’s guide – Manning Publications (2023)
English | eBook | Size: 14.08 MB
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 transfer your software development skills to deep learning systems, recognize and solve common engineering challenges for these systems, and 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.
About the Technology
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.
About the Book
Designing 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 Inside
The deep learning development cycle
Automate training in TensorFlow and PyTorch
Dataset management, model serving, and hyperparameter tuning
A hands-on deep learning lab
About the Authors
Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO.
Chi Wang and Donald Szeto
Foreword by Silvio Savarese and Caiming Xiong
June 2023 | ISBN: 9781633439863 | 360 pages
RAPIDGATOR
https://rapidgator.net/file/76fe00cb507e3ba643cacc85ffe25508/Designing_Deep_Learning_Systems_-_A_software_engineer's_guide_-_Manning_Publications_(2023).rar.html
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