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Learn to build end-to-end AI apps from scratch for Android and iOS
using TensorFlow Lite, CoreML, and PyTorch Key Features Build
practical, real-world AI projects on Android and iOS Implement
tasks such as recognizing handwritten digits, sentiment analysis,
and more Explore the core functions of machine learning, deep
learning, and mobile vision Book DescriptionWe're witnessing a
revolution in Artificial Intelligence, thanks to breakthroughs in
deep learning. Mobile Artificial Intelligence Projects empowers you
to take part in this revolution by applying Artificial Intelligence
(AI) techniques to design applications for natural language
processing (NLP), robotics, and computer vision. This book teaches
you to harness the power of AI in mobile applications along with
learning the core functions of NLP, neural networks, deep learning,
and mobile vision. It features a range of projects, covering tasks
such as real-estate price prediction, recognizing hand-written
digits, predicting car damage, and sentiment analysis. You will
learn to utilize NLP and machine learning algorithms to make
applications more predictive, proactive, and capable of making
autonomous decisions with less human input. In the concluding
chapters, you will work with popular libraries, such as TensorFlow
Lite, CoreML, and PyTorch across Android and iOS platforms. By the
end of this book, you will have developed exciting and more
intuitive mobile applications that deliver a customized and more
personalized experience to users. What you will learn Explore the
concepts and fundamentals of AI, deep learning, and neural networks
Implement use cases for machine vision and natural language
processing Build an ML model to predict car damage using TensorFlow
Deploy TensorFlow on mobile to convert speech to text Implement GAN
to recognize hand-written digits Develop end-to-end mobile
applications that use AI principles Work with popular libraries,
such as TensorFlow Lite, CoreML, and PyTorch Who this book is
forMobile Artificial Intelligence Projects is for machine learning
professionals, deep learning engineers, AI engineers, and software
engineers who want to integrate AI technology into mobile-based
platforms and applications. Sound knowledge of machine learning and
experience with any programming language is all you need to get
started with this book.
Bring magic to your mobile apps using TensorFlow Lite and Core ML
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
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 learn
Demystify the machine learning landscape on mobile Age and gender
detection using TensorFlow Lite and Core ML Use ML Kit for Firebase
for in-text detection, face detection, and barcode scanning Create
a digit classifier using adversarial learning Build a
cross-platform application with face filters using OpenCV Classify
food using deep CNNs and TensorFlow Lite on iOS Who 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.
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