Deep Learning Patterns and Practices

English | 2021 | ISBN: 978-1617298264 | 472 Pages | PDF, EPUB, MOBI | 46 MB

Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.

In Deep Learning Patterns and Practices you will learn:

• Internal functioning of modern convolutional neural networks
• Procedural reuse design pattern for CNN architectures
• Models for mobile and IoT devices
• Assembling large-scale model deployments
• Optimizing hyperparameter tuning
• Migrating a model to a production environment

The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. 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. Each valuable technique is presented in a way that’s easy to understand and filled with accessible diagrams and code samples.

Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example.

Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects.

What’s inside

• Modern convolutional neural networks
• Design pattern for CNN architectures
• Models for mobile and IoT devices
• Large-scale model deployments
• Examples for computer vision