Book Image

Mobile Artificial Intelligence Projects

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

Mobile Artificial Intelligence Projects

By: Karthikeyan NG, Arun Padmanabhan, Matt R. 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)
PyTorch Experiments on NLP and RNN
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
Implementing GANs to Recognize Handwritten Digits

AI versus machine learning versus deep learning

AI is no new term given the plethora of articles we read online and the many movies based on it. So, before we proceed any further, let's take a step back and understand AI and the terms that regularly accompany it from a practitioner's point of view. We will get a clear distinction of what machine learning, deep learning, and AI are, as these terms are often used interchangeably:

AI is the capability that can be embedded into machines that allows machines to perform tasks that are characteristic of human intelligence. These tasks include seeing and recognizing objects, listening and distinguishing sounds, understanding and comprehending language, and other similar tasks.

Machine learning (ML) is a subset of AI that encompasses techniques used to make these human-like tasks possible. So, in a way, ML is what is used to achieve AI.

In essence, if we did not use ML to achieve these tasks, then we would actually be trying to write millions of lines of code with complex loops, rules, and decision trees.

ML gives machines the ability to learn without being explicitly programmed. So, instead of hardcoding rules for every possible scenario to a task, we simply provide examples of how the task is done versus how it should not be done. ML then trains the system on this provided data so it can learn for itself.

ML is an approach to AI where we can achieve tasks such as grouping or clustering, classifying, recommending, predicting, and forecasting data. Some common examples of this are classifying spam mail, stock market predictions, weather forecasting, and more.

Deep learning is a special technique in ML that emulates the human brain's biological structure and works to accomplish human-like tasks. This is done by building a network of neurons just like in the brain through an algorithmic approach using ANNs, which are stack of algorithms that can solve problems at human-like efficiency or better.

These layers are commonly referenced as deepnets (deep architectures) and each has a specific problem that it can be trained to solve. The deep learning space is currently at the cutting edge of what we see today, with applications such as autonomous driving, Alexa and Siri, machine vision, and more.

Throughout this book, we will be executing tasks and building apps that are built using these deepnets, and we will also solve use cases by building our very own deepnet architecture.