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)

Describing Images in Natural Language

If image classification and object detection are intelligent tasks, describing an image in natural language is definitely a more challenging task that requires more intelligence – just think for a moment about how everyone grows from a newborn (who learns to recognize objects and detect their locations) to a three-year old (who learns to tell a story about a picture). The official term for the task of describing an image in natural language is image captioning. Unlike speech recognition, which has a long history of research and development, image captioning (with full natural language, not just keyword output) has only had a short but exciting history of research due to its complexityand the deep learning breakthrough in 2012.

In this chapter, we'll first review how a deep learning-based image captioning model that won the 2015...