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Machine Learning for Mobile

Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu
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Machine Learning for Mobile

Machine Learning for Mobile

By: Revathi Gopalakrishnan, Avinash Venkateswarlu

Overview of this book

Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
Table of Contents (14 chapters)
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12
Question and Answers

Features of Google Cloud Vision


Google Cloud Vision API comprises various complex and powerful machine learning models that help to perform image analysis. It classifies images into various categories using an easy-to-use REST API. The important features provided by Google Cloud Vision include the following:

  • Label detection: This enables us to classify images into thousands of categories. The images can be categorized into various common category labels, such as animals and fruits.
  • Image attribute detection: This enables us to detect individual objects from within images. It can also detect attributes such as prominent color.
  • Face detection: This enables us to detect faces from within images. If there are multiple faces in the images, each can be detected individually. It can also detect the prominent attributes associated with a face, such as wearing a helmet.
  • Logo detection: This enables us to detect printed words from images. Prominent logos are trained which can be detected.
  • Landmark detection...

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