Building Mobile Apps with TensorFlow

Deep learning is an incredibly powerful technology for understanding messy data from the real world. TensorFlow was designed from the ground up to harness that power inside mobile applications on platforms like Android and iOS. In this guide, I’ll show you how to integrate it effectively.

Challenges of Building a Mobile App with TensorFlow

This guide is for developers who have a TensorFlow model successfully working in a desktop environment and who want to integrate it into a mobile application. Here are the main challenges you’ll face during that process:

  • Understanding the basics of TensorFlow

  • Building TensorFlow for your platform

  • Integrating the TensorFlow library into your application

  • Preparing your model file for mobile deployment

  • Optimizing for latency, RAM usage, model file size, and binary size

  • Exploring quantized calculations

In this guide, I cover all of these areas, with detailed breakdowns of what you need to know within each chapter.

Understanding the Basics of TensorFlow

In this section, we’ll look at how TensorFlow works and what sort of problems you can use it to solve.

What Is TensorFlow?

It’s recently become possible to solve a range of problems across a wide variety of domains using large neural networks. TensorFlow is a framework that lets you train and deploy these networks. It was originally created by Google as its main internal tool for deep learning, but it’s also available as open source with a large ...

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