Book Image

Mobile Artificial Intelligence Projects

By : NG, Padmanabhan, Matt Cole
Book Image

Mobile Artificial Intelligence Projects

By: NG, 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

Popular ML–based cloud services

To begin your ML journey, you can use one of the existing cloud-based services on ML. ML as a Service (MLaaS) is widely used across all contemporary business sectors. Data is becoming cheaper, and data volume is growing exponentially. As a result, the processing power of devices is increasing at a much quicker rate. This trend has made way for multiple cloud-based services, such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), now joined by MLaaS.

Even though we can run an ML model on our mobile device, it is still greatly impacted by limited memory, CPU and Graphics Processing Unit (GPU) resources. In this case, a cloud service comes in handy.

Starting with ML in the cloud, there are multiple services available, such as facial recognition, optical character recognition, image recognition...