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.
