Deep Learning Patterns and Practices

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

    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 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

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.

About the book

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

About the reader

For machine learning engineers familiar with Python and deep learning.

About the author

Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations.

Table of Contents

PART 1 DEEP LEARNING FUNDAMENTALS

1 Designing modern machine learning

2 Deep neural networks

3 Convolutional and residual neural networks

4 Training fundamentals

PART 2 BASIC DESIGN PATTERN

5 Procedural design pattern

6 Wide convolutional neural networks

7 Alternative connectivity patterns

8 Mobile convolutional neural networks

9 Autoencoders

PART 3 WORKING WITH PIPELINES

10 Hyperparameter tuning

11 Transfer learning

12 Data distributions

13 Data pipeline

14 Training and deployment pipeline