Deep Learning Design Patterns
Andrew Ferlitsch
  • MEAP began July 2020
  • Publication in Spring 2021 (estimated)
  • ISBN 9781617298264
  • 400 pages (estimated)
  • printed in black & white

A definitive book by a world-class expert on best practices in the development of deep learning systems.

Dr. Brian R. Gaines
Deep learning has revealed ways to create algorithms for applications that we never dreamed were possible. For software developers, the challenge lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Design Patterns is here to help. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Written by Google deep learning expert Andrew Ferlitsch, it's filled with the latest deep learning insights and best practices from his work with Google Cloud AI. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples.

About the Technology

You don't need to design your deep learning applications from scratch! By viewing cutting-edge deep learning models as design patterns, developers can speed up their creation of AI models and improve model understandability for both themselves and other users.

About the book

Deep Learning Design Patterns distills models from the latest research papers into practical design patterns applicable to enterprise AI projects. Using diagrams, code samples, and easy-to-understand language, Google Cloud AI expert Andrew Ferlitsch shares insights from state-of-the-art neural networks. You'll learn how to integrate design patterns into deep learning systems from some amazing examples, including a real-estate program that can evaluate house prices just from uploaded photos and a speaking AI capable of delivering live sports broadcasting. Building on your existing deep learning knowledge, you'll quickly learn to incorporate the very latest models and techniques into your apps as idiomatic, composable, and reusable design patterns.

What's inside

  • Internal functioning of modern convolutional neural networks
  • Procedural reuse design pattern for CNN architectures
  • Models for mobile and IoT devices
  • Composable design pattern for automatic learning methods
  • Assembling large-scale model deployments
  • Complete code samples and example notebooks
  • Accompanying YouTube videos

About the reader

For machine learning engineers familiar with Python and deep learning.

About the author

Andrew Ferlitsch is an expert on computer vision and deep learning at Google Cloud AI Developer Relations. He was formerly a principal research scientist for 20 years at Sharp Corporation of Japan, where he amassed 115 US patents and worked on emerging technologies in telepresence, augmented reality, digital signage, and autonomous vehicles. In his present role, he reaches out to developer communities, corporations and universities, teaching deep learning and evangelizing Google's AI technologies.

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