Machine Learning Projects for Mobile Applications
Packt Publishing Limited (Verlag)
978-1-78899-459-0 (ISBN)
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 DescriptionMachine 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 forMachine 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.
Karthikeyan NG is the Head of Engineering and Technology at the Indian lifestyle & fashion retail brand. He served as a software engineer at Symantec Corporation and has worked with 2 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.
Table of Contents
Mobile Landscapes in Machine Learning
CNN Based Age and Gender Identification Using Core ML
Applying Neural Style Transfer on Photos
Deep Diving into the ML Kit with Firebase
A Snapchat-Like AR Filter on Android
Handwritten Digit Classifier Using Adversarial Learning
Face-Swapping with Your Friends Using OpenCV
Classifying Food Using Transfer Learning
What's Next?
| Erscheinungsdatum | 05.11.2018 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Software Entwicklung ► Mobile- / App-Entwicklung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-78899-459-0 / 1788994590 |
| ISBN-13 | 978-1-78899-459-0 / 9781788994590 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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