Book Image

Machine Learning Projects for Mobile Applications

By : Karthikeyan NG
Book Image

Machine Learning Projects for Mobile Applications

By: Karthikeyan NG

Overview of this book

Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google’s ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN. By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.
Table of Contents (16 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Contributors

About the author

Karthikeyan NG is the Head of Engineering and Technology at the Indian lifestyle and fashion retail brand. He served as a software engineer at Symantec Corporation and has worked with two US-based startups as an early employee and has built various products. He has 9+ years of experience in various scalable products using Web, Mobile, ML, AR, and VR technologies. He is an aspiring entrepreneur and technology evangelist. His interests lie in exploring new technologies and innovative ideas to resolve a problem. He has also bagged prizes from more than 15 hackathons, is a TEDx speaker and a speaker at technology conferences and meetups as well as guest lecturer at a Bengaluru University. When not at work, he is found trekking.

I would like to thank Saurav Satpathy for helping me with the codes in one of the chapters. I would like to extend my gratitude to Varsha Shetty for presenting the idea of the book, and to Rhea Henriques for her tenacity. Thanks to Akshi, Tejas, Sayli, and the technical reviewer, Mayur, and the editorial team. I would also like to thank the open source community for making this book possible with the frameworks on both Android and iOS platforms.

 

About the reviewer

Mayur Ravindra Narkhedehas a good blend of experience in data science and industrial domain. He is a researcher with a B.Tech in computer science and an M.Tech in CSE with a specialization in Artificial Intelligence.

A data scientist whose core experience lies in building automated end-to-end solutions, he is proficient at applying technology, AI, ML, data mining, and design thinking to better understand and predict improvements in business functions and desirable requirements with growth profitability.

He has worked on multiple advanced solutions, such as ML and predictive model development for the oil and gas industry, financial services, road traffic and transport, life sciences, and the big data platform for asset-intensive industries.

 

 

 

 

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