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 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.

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

Review

“One of the best deep learning books I have read.” 

—Muhammad Sohaib Arif, Tek Systems 

“A must-read for anyone trying to understand the ins and outs of deep learning and best practices for building a machine learning pipeline.” 

—Ariel Gamiño, GLG 

“Really good for the modern deep learning professional. Suggested for those who want to understand what’s under the hood.” 

—Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland 

“Whether you are new to AI or have some knowledge of it, this will be your companion on the path to expertise.” 

—Eros Pedrini, everis

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.