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    Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Anubhav Singh, Rimjhim Bhadani
    Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS Anubhav Singh, Rimjhim Bhadani - okladka książki

    Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS Anubhav Singh, Rimjhim Bhadani - okladka książki

    Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS Anubhav Singh, Rimjhim Bhadani - audiobook MP3

    Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS Anubhav Singh, Rimjhim Bhadani - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    380
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more.

    With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment.

    By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.

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    O autorach książki

    Anubhav Singh, a web developer since before Bootstrap was launched, is an explorer of technologies, often pulling off crazy combinations of uncommon tech. An international rank holder in the Cyber Olympiad, he started off by developing his own social network and search engine as his first projects at the age of 15, which stood among the top 500 websites of India during their operational years. He's continuously developing software for the community in domains with roads less walked on. You can often catch him guiding students on how to approach ML or the web, or both together. He's also the founder of The Code Foundation, an AI-focused start-up. Anubhav is a Venkat Panchapakesan Memorial Scholarship awardee and an Intel Software Innovator.
    Rimjhim Bhadani is an open source lover. She has always believed in making the resources accessible to everyone at a minimal cost. She is a big fan of Mobile Application Development and has developed a number of projects most which aim to solve major and minor daily life challenges. She has been an Android mentor in Google Code-In and an Android developer for Google Summer of Code. Supporting her vision to serve the community, she is one among the six Indian students to be recognized as Google Venkat Panchapakesan Memorial Scholar and one among the three Indian students to be awarded the Grace Hopper Student Scholarship in 2019.

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