Book Image

Mobile Artificial Intelligence Projects

By : Karthikeyan NG, Arun Padmanabhan, Matt Cole
Book Image

Mobile Artificial Intelligence Projects

By: Karthikeyan NG, Arun Padmanabhan, Matt Cole

Overview of this book

We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users.
Table of Contents (12 chapters)
6
PyTorch Experiments on NLP and RNN
7
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
8
Implementing GANs to Recognize Handwritten Digits

WaveNet

WaveNet is a deep generative network that is used to generate raw audio waveforms. Sounds waves are generated by WaveNet to mimic the human voice. This generated sound is more natural than any of the currently existing text-to-speech systems, reducing the gap between system and human performance by 50%.

With a single WaveNet, we can differentiate between multiple speakers with equal fidelity. We can also switch between individual speakers based on their identity. This model is autoregressive and probabilistic, and it can be trained efficiently on thousands of audio samples per second. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning the speaker identity.

As shown in the movie Her, the long-standing dream of human-computer interaction is to allow people to talk to machines. The...