Machine Learning On Mobile Devices
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Machine Learning On Mobile Devices
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Author : Dr Quinn Miles
language : en
Publisher: Independently Published
Release Date : 2025-09-16
Machine Learning On Mobile Devices written by Dr Quinn Miles and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-16 with Computers categories.
Unlock the Power of Machine Learning-Right on Your Mobile Device Are you curious about artificial intelligence but feel intimidated by all the technical jargon? Have you dreamed of building smart apps for your phone but worry you need years of experience or a background in coding? You're not alone-and you're exactly who this book was written for. Machine Learning on Mobile Devices: A Practical Guide to TensorFlow, Core ML, and TensorFlow Lite makes modern AI approachable, practical, and-above all-fun. Whether you're a complete beginner, a student, or a developer hoping to add cutting-edge skills to your toolkit, this step-by-step guide is your friendly, confidence-building companion. No Experience Needed-Just Curiosity! You don't need to be a math genius or seasoned programmer to get started. Inside, you'll discover a warm, accessible journey through the world of on-device machine learning, designed to encourage experimentation and celebrate every milestone-no matter how small. What You'll Gain: Practical Skills: Learn how to build, optimize, and deploy real machine learning models on Android and iOS using TensorFlow Lite and Core ML. Step-by-Step Guidance: Clear, hands-on tutorials walk you through every process, from your very first neural network to advanced deployment strategies. Real-World Applications: Explore image recognition, voice processing, translation, and more-directly on your phone or tablet. Expert Insights Made Simple: Technical terms and concepts are broken down in plain English, so you never feel lost or overwhelmed. Beginner-Friendly Approach: Mistakes are normalized and small victories are celebrated-because every great coder started somewhere. Key Features: Understand the fundamentals of machine learning without getting buried in theory. Gain confidence working with TensorFlow Lite, Core ML, and real mobile AI projects. Build your own apps that work offline, protect privacy, and deliver instant results. Tackle troubleshooting, model optimization, and deployment with clear, concise solutions. Who Is This Book For? Beginners with zero technical background Students exploring data science, AI, or app development Developers looking to bring smart features to mobile apps Anyone eager to learn by doing-at their own pace Take the Leap-Your Mobile AI Journey Starts Here! Stop letting fear or self-doubt hold you back from learning the technology shaping our future. With this book by your side, you'll discover that machine learning on mobile devices is not just possible-it's rewarding, empowering, and within your reach. Start building the future in your pocket-one project at a time. begin your hands-on AI adventure today!
Machine Learning For Mobile
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Author : Revathi Gopalakrishnan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-31
Machine Learning For Mobile written by Revathi Gopalakrishnan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-31 with Computers categories.
Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease Key FeaturesBuild smart mobile applications for Android and iOS devicesUse popular machine learning toolkits such as Core ML and TensorFlow LiteExplore cloud services for machine learning that can be used in mobile appsBook Description Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices. What you will learnBuild intelligent machine learning models that run on Android and iOSUse machine learning toolkits such as Core ML, TensorFlow Lite, and moreLearn how to use Google Mobile Vision in your mobile appsBuild a spam message detection system using Linear SVMUsing Core ML to implement a regression model for iOS devicesBuild image classification systems using TensorFlow Lite and Core MLWho this book is for If you are a mobile app developer or a machine learning enthusiast keen to use machine learning to build smart mobile applications, this book is for you. Some experience with mobile application development is all you need to get started with this book. Prior experience with machine learning will be an added bonus
Machine Learning Projects For Mobile Applications
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Author : Karthikeyan NG
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31
Machine Learning Projects For Mobile Applications written by Karthikeyan NG and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.
Bring magic to your mobile apps using TensorFlow Lite and Core ML Key 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 Description Machine 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 iOS Who this book is for Machine 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.
Ai And Machine Learning On Device Development
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Author : Laurence Moroney
language : en
Publisher:
Release Date : 2021
Ai And Machine Learning On Device Development written by Laurence Moroney and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating models and running them on popular mobile platforms such as iOS and Android. Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today. Explore the options for implementing ML and AI on mobile devices--and when to use each Create ML models for iOS and Android Write ML Kit and TensorFlow Lite apps for iOS and Android and Core ML/Create ML apps for iOS Understand how to choose the best techniques and tools for your use case: on-device inference versus cloud-based inference, high-level APIs versus low-level APIs, and more Learn privacy and ethics best practices for ML on devices.
Machine Learning For Networking
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Author : Éric Renault
language : en
Publisher: Springer Nature
Release Date : 2021-03-02
Machine Learning For Networking written by Éric Renault and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-02 with Computers categories.
This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.
Data Mining Mobile Devices
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Author : Jesus Mena
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Data Mining Mobile Devices written by Jesus Mena and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Business & Economics categories.
With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertainin
Novel Financial Applications Of Machine Learning And Deep Learning
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Springer Nature
Release Date : 2023-03-01
Novel Financial Applications Of Machine Learning And Deep Learning written by Mohammad Zoynul Abedin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-01 with Business & Economics categories.
This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Artificial Neural Networks And Machine Learning Icann 2025
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Author : Walter Senn
language : en
Publisher: Springer Nature
Release Date : 2025-10-12
Artificial Neural Networks And Machine Learning Icann 2025 written by Walter Senn and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-12 with Computers categories.
The four-volume set LNCS 16068-16071 constitutes the proceedings of the 34th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2025, held in Kaunas, Lithuania, September 9–12, 2025. The 170 full papers and 8 abstracts included in these conference proceedings were carefully reviewed and selected from 375 submissions. The conference strongly values the synergy between theoretical progress and impactful real-world applications, and actively encourages contributions that demonstrate how artificial neural networks are being used to address pressing societal and technological challenges.
Research Outlook Innovations Research Trends In Science Technology
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Author : Prof S S Rajput
language : en
Publisher: Alborear (OPC) Pvt. Ltd.
Release Date : 2020-10-25
Research Outlook Innovations Research Trends In Science Technology written by Prof S S Rajput and has been published by Alborear (OPC) Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-25 with Science categories.
This book is focused predominantly on academicians, research scholars belong to science and engineering, managers, scientists, technicians, and other professionals in the field of qualitative research. This book is comprehended from different sources of research in Science and Technology. On the first occasion, the task of providing researchers with a broad view of the relationship between science and technology. The second reason for writing the book was the need to fill a gap in academics and research. While many excellent books, documents, and article exist for innovative practices, we have not found a work in which we can properly understand the content that the researcher needs to understand. So, after much deliberation, we decided to collect all quality efforts in one string. At the most basic level, this book is trying to show research scholars; what science, technology, and innovations are all about. It cannot study or gain knowledge of that part and is at a level that most researchers should find clear and understandable. Our goal was to develop content that will help researchers who are beginning to use innovative practices. We hope to meet the needs of academicians, research scholars who are being encouraged to incorporate more reading and writing in the field of science and technology. In summary, this book is targeted to the needs of individuals engaged in quality research activities in science and technology. Our goal is to present the topics of creativity and innovation to this audience in a way that enables them to incorporate new skills into their daily work. We would like to thank all the contributors who have made the production of this book so fascinating and enjoyable. Their scholarship and dedicated commitment and motivation to ‘getting it right’ are the keys to the book’s quality, and we greatly appreciate their good nature over many months in the face of our editorial demands and time limits. We are also grateful for using their texts, ideas, and critical remarks We would also like to thank Prof Dr Nilam N Ghuge, Prof Dr D Ayub Khan Dawood, Prof Dr Vilas A Pharande, all reviewers and all authors for their help in consolidating the interdisciplinary of the book. We are grateful to all the 18 institutions for their support. It will not be possible to bring out this edition.
Internet Of Vehicles Technologies And Services
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Author : Ching-Hsien Hsu
language : en
Publisher: Springer
Release Date : 2017-01-19
Internet Of Vehicles Technologies And Services written by Ching-Hsien Hsu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-19 with Computers categories.
This book constitutes the refereed proceedings of the Third International Conference on Internet of Vehicles, IOV 2016, held in Nadi, Fiji, in December 2016. The 22 full papers presented were carefully reviewed and selected from 55 submissions. IOV 2016 is intended to play an important role for researchers and industry practitioners to exchange information regarding advancements in the state of art and practice of IOV architectures, protocols, services, and applications, as well as to identify emerging research topics and define the future directions of IOV.