Mobile Artificial Intelligence Projects
Develop seven projects on your smartphone using artificial intelligence and deep learning techniques
Published on 30. March 2019
Book
Paperback/Softback
312 pages
978-1-78934-407-3 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 17 mm
Weight
586 gr
ISBN-13
978-1-78934-407-3 (9781789344073)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Karthikeyan NG | Arun Padmanabhan | Matt Cole
Mobile Artificial Intelligence Projects
Develop seven projects on your smartphone using artificial intelligence and deep learning techniques
E-Book
09/2024
Packt Publishing
from
€39.29
Available for download
Persons
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. Arun Padmanabhan is a Machine Learning consultant with over 8 years of experience building end-to-end machine learning solutions and applications. Currently working with a couple of start-ups in the Financial and Insurance industries, he specializes in automating manual workflows using AI and creating Machine Vision and NLP applications. In past, he has led the data science team of a Singapore based product startup in the restaurant domain. He also has built stand-alone and integrated Machine Learning solutions in the Manufacturing, Shipping and e-commerce domains over the years. His interests are in research, development and applications of Artificial Intelligence and Deep Architectures. Matt R. Cole is a developer and author with 30 years' experience. Matt is the owner of Evolved AI Solutions, a provider of advanced Machine Learning/Bio-AI, Microservice and Swarm technologies. Matt is recognized as a leader in Microservice and Artificial Intelligence development and design. As an early pioneer of VOIP, Matt developed the VOIP system for NASA for the International Space Station and Space Shuttle. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. In his spare time Matt authors books, and continues his education taking every available course in advanced mathematics, AI/ML/DL, Quantum Mechanics/Physics, String Theory and Computational Neuroscience.
Content
Table of Contents
Artificial Intelligence Concepts and Fundamentals
Creating a Real-Estate price prediction mobile app
Implementing Deepnet Architectures to Recognize Hand Written Digits
Building a Machine Vision Mobile App to Classify Flower Species
Building a ML Model to Predict Car Damage Using TensorFlow
PyTorch experiments on NLP and RNN
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
Implementing GANs to Recognize Handwritten Digits
Sentiment Analysis over Text Using LinearSVC
What's next?
Artificial Intelligence Concepts and Fundamentals
Creating a Real-Estate price prediction mobile app
Implementing Deepnet Architectures to Recognize Hand Written Digits
Building a Machine Vision Mobile App to Classify Flower Species
Building a ML Model to Predict Car Damage Using TensorFlow
PyTorch experiments on NLP and RNN
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
Implementing GANs to Recognize Handwritten Digits
Sentiment Analysis over Text Using LinearSVC
What's next?