Manning Publications | Designing Deep Learning Systems: A Software Engineer's Guide (2023 EN)

Author: Chi Wang, Donald Szeto
Full Title: Designing Deep Learning Systems: A Software Engineer's Guide
Publisher: Manning Publications (July 25, 2023)
Year: 2023
ISBN-13: 9781633439863
ISBN-10: 1633439860
Pages: 360
Language: English
Genre: IT: Machine Learning
File type: EPUB (True), PDF (True), Code Files
Quality: 10/10
Price: $59.99


A vital guide to building the platforms and systems that bring deep learning models to production.

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

Deep 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 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. Th is 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 reader:
For software developers and engineering-minded data scientists. Examples in Java and Python.

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