Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML - Couverture souple

NG, Karthikeyan

Couverture souple

ISBN 10 : 1788994590 ISBN 13 : 9781788994590

Editeur : Packt Publishing, 2018

Synopsis

Bring magic to your mobile apps using TensorFlow Lite and Core ML

Key Features

• Explore machine learning using classification, analytics, and detection tasks.
• Work with image, text and video datasets to delve into real-world tasks
• Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite

Book Description

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.

What you will learn

• Demystify the machine learning landscape on mobile
• Age and gender detection using TensorFlow Lite and Core ML
• Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning
• Create a digit classifier using adversarial learning
• Build a cross-platform application with face filters using OpenCV
• Classify food using deep CNNs and TensorFlow Lite on iOS

Who this book is for

Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.

Table of Contents

1. Mobile Landscapes in Machine Learning
2. CNN Based Age and Gender Identification Using Core ML
3. Applying Neural Style Transfer on Photos
4. Deep Diving into the ML Kit with Firebase
5. A Snapchat-Like AR Filter on Android
6. Handwritten Digit Classifier Using Adversarial Learning
7. Face-Swapping with Your Friends Using OpenCV
8. Classifying Food Using Transfer Learning
9. What's Next?

A propos du livre

À propos de l?auteur

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.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2018
• Langue: anglais
• ISBN 10: 1788994590
• ISBN 13: 9781788994590
• Reliure: Broché
• Nombre de pages: 246
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible