hiapps / mlmobileapps

Repository for Machine Learning Projects for Mobile Applications book

MLmobileapps

Repository for Machine Learning Projects for Mobile Applications book. Book is available here: https://www.amazon.com/Machine-Learning-Projects-Mobile-Applications/dp/1788994590

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.

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.

By Karthikeyan NG

Table of Contents & what will you learn

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?