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    1. Data och IT
    2. Systemvetenskap och AI

    Machine Learning Projects for Mobile Applications

    Build Android and iOS applications using TensorFlow Lite and Core ML

    AvKarthikeyan NG

    Häftad, Engelska, 2018

    585 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Beskrivning

    Bring magic to your mobile apps using TensorFlow Lite and Core MLKey FeaturesExplore machine learning using classification, analytics, and detection tasks.Work with image, text and video datasets to delve into real-world tasksBuild apps for Android and iOS using Caffe, Core ML and Tensorflow LiteBook 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 learnDemystify the machine learning landscape on mobileAge and gender detection using TensorFlow Lite and Core MLUse ML Kit for Firebase for in-text detection, face detection, and barcode scanningCreate a digit classifier using adversarial learningBuild a cross-platform application with face filters using OpenCVClassify food using deep CNNs and TensorFlow Lite on iOSWho 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.

    Produktinformation

    • Utgivningsdatum:2018-10-31
    • Mått:191 x 235 x 16 mm
    • Vikt:480 g
    • Format:Häftad
    • Språk:Engelska
    • Antal sidor:246
    • Förlag:Packt Publishing Limited
    • ISBN:9781788994590

    Utforska kategorier

    • Systemvetenskap och AI inom Data och IT
    • Programmeringsböcker inom Data och IT

    Mer om författaren

    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 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.

    Innehållsförteckning

    • Table of ContentsMobile Landscapes in Machine LearningCNN Based Age and Gender Identification Using Core MLApplying Neural Style Transfer on PhotosDeep Diving into the ML Kit with FirebaseA Snapchat-Like AR Filter on AndroidHandwritten Digit Classifier Using Adversarial LearningFace-Swapping with Your Friends Using OpenCVClassifying Food Using Transfer LearningWhat's Next?