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Intelligent Mobile Projects with TensorFlow

Intelligent Mobile Projects with TensorFlow

By : Tang
5 (4)
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Intelligent Mobile Projects with TensorFlow

Intelligent Mobile Projects with TensorFlow

5 (4)
By: 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)
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Understanding Simple Speech Commands

Voice services, such as Apple Siri, Amazon Alexa, Google Assistant, and Google Translate, have become more and more popular these days, as voice is the most natural and effective way for us to find information or accomplish tasks in certain scenarios. Many of those voice services are cloud-based because user speech can be pretty long and freeform, and automatic speech recognition (ASR) is very complicated and requires a lot of computing power. In fact, only in recent years has ASR in natural and noisy environments become feasible thanks to the breakthrough in deep learning.

But in some cases, it makes sense to be able to recognize simple speech commands offline on a device. For example, to control the movement of a Raspberry-Pi-driven robot, you don't need complicated voice commands, and not only is on-device ASR faster than a cloud-based...

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