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LEADER 00000nam a2200409 a 4500 
003    CaSebORM 
005    20200417124247.9 
006    m     o  d         
007    cr cn          
008    170420s2020    xx      o           eng   
024 8  9781789611212 
035    (CaSebORM)9781789611212 
041 0  eng 
100 1  Singh, Anubhav,|eauthor. 
245 10 Mobile Deep Learning with TensorFlow Lite, ML Kit and 
       Flutter|h[O'Reilly electronic resource] /|cSingh, Anubhav.
250    1st edition 
264  1 |bPackt Publishing,|c2020. 
300    1 online resource (380 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file 
365    |b39.99 
520    Learn how to deploy effective deep learning solutions on 
       cross-platform applications built using TensorFlow Lite, 
       ML Kit, and Flutter Key Features Work through projects 
       covering mobile vision, style transfer, speech processing,
       and multimedia processing Cover interesting deep learning 
       solutions for mobile Build your confidence in training 
       models, performance tuning, memory optimization, and 
       neural network deployment through every project Book 
       Description 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. What you will learn Create your own 
       customized chatbot by extending the functionality of 
       Google Assistant Improve learning accuracy with the help 
       of features available on mobile devices Perform visual 
       recognition tasks using image processing Use augmented 
       reality to generate captions for a camera feed 
       Authenticate users and create a mechanism to identify rare
       and suspicious user interactions Develop a chess engine 
       based on deep reinforcement learning Explore the concepts 
       and methods involved in rolling out production-ready deep 
       learning iOS and Android applications Who this book is for
       This book is for data scientists, deep learning and 
       computer vision engineers, and natu... 
533    Electronic reproduction.|bBoston, MA :|cSafari,|nAvailable
       via World Wide Web.|d2020. 
538    Mode of access: World Wide Web. 
542    |fCopyright © 2020 Packt Publishing|g2020 
550    Made available through: Safari, an O'Reilly Media Company.
588 00 Online resource; Title from title page (viewed April 6, 
655  7 Electronic books.|2local 
700 1  Bhadani, Rimjhim,|eauthor. 
710 2  Safari, an O'Reilly Media Company. 
856 40 |zConnect to this resource online|uhttps://