Book Image

Intelligent Mobile Projects with TensorFlow

By : Jeff Tang
Book Image

Intelligent Mobile Projects with TensorFlow

By: Jeff Tang

Overview of this book

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips.
Table of Contents (14 chapters)

Getting Started with Mobile TensorFlow

This chapter covers how to get your development environments set up for building all the iOS or Android apps with TensorFlow that are discussed in the rest of the book. We won't discuss in detail all the supported TensorFlow versions, OS versions, Xcode, and Android Studio versions that can be used for development, as that kind of information can easily be found on the TensorFlow website ( or via Google. Instead, we'll just talk briefly about sample working environments in this chapter so that we can dive in quickly to look at all the amazing apps you can build with the environments.

If you already have TensorFlow, Xcode, and Android Studio installed, and can run and test the sample TensorFlow iOS and Android apps, and if you already have an NVIDIA GPU installed for faster deep learning model training, you can skip this chapter. Or you can jump directly to the section that you're unfamiliar with.

We're going to cover the following topics in this chapter (how to set up the Raspberry Pi development environment will be discussed in Chapter 12, Developing TensorFlow Apps on Raspberry Pi):

  • Setting up TensorFlow
  • Setting up Xcode
  • Setting up Android Studio
  • TensorFlow Mobile vs TensorFlow Lite
  • Running sample TensorFlow iOS apps
  • Running sample TensorFlow Android apps