Federated Learning With Python
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Federated Learning With Python
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Author : Kiyoshi Nakayama PhD
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-28
Federated Learning With Python written by Kiyoshi Nakayama PhD 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 2022-10-28 with Computers categories.
Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level Key FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook Description Federated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples. FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature. By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments. What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is for This book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.
Big Data Over Networks
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Author : Shuguang Cui
language : en
Publisher: Cambridge University Press
Release Date : 2016-01-14
Big Data Over Networks written by Shuguang Cui and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-14 with Computers categories.
Examines the crucial interaction between big data and communication, social and biological networks using critical mathematical tools and state-of-the-art research.
Building Android Apps In Python Using Kivy With Android Studio
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Author : Ahmed Fawzy Mohamed Gad
language : en
Publisher: Apress
Release Date : 2019-10-14
Building Android Apps In Python Using Kivy With Android Studio written by Ahmed Fawzy Mohamed Gad and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Computers categories.
Start building Python-based Android applications using Kivy with Android Studio. Through in-depth examples, this book teaches you everything you need to create your first Android application in Python and publish on Google Play. Building Android Apps in Python Using Kivy with Android Studio takes you through the basics of Kivy by discussing its application structure, widgets, and event handling. The KV language is then introduced for separating the logic and GUI by adding widgets within a KV file. You will then learn how to utilize Android camera using Kivy, build the HTTP server using Flask, and create and manage multiple screens to help you design your own applications. Through detailed step-by-step instructions, you will create your first multi-level cross-platform game that includes animation and sound effects. Following this, the process of converting the Kivy application into an Android application using Buildozer and Python-4-Android is covered in detail. You will then learn how to edit the generated Android Studio project into Android Studio by adding extensions to the original application. The widgets added in Kivy could be handled within Android Studio. Moreover, Android views could be added to enrich the Kivy application. The resulting Android application created with Kivy can be hosted on Google Play to download and install as a regular Android application. At the end, this book will give you the basic knowledge of Kivy needed to build cross-platform Android applications, produce an Android Studio project, and understand how it all works in detail. What You Will Learn Build cross-platform applications from scratch using Kivy in detail Create a cross-platform interactive multi-level game from the ground up Examine the pipeline of building an Android app from the Python Kivy app Understand the structure of the Android Studio project produced by Kivy Recognize how to extend the application within Android Studio by adding more Android views to the application main activity. Who This Book Is For Python developers with no previous experience in Kivy who are looking to create their first Android application completely in Python.
Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Federated Learning Systems
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Author : Muhammad Habib ur Rehman
language : en
Publisher: Springer Nature
Release Date : 2021-06-11
Federated Learning Systems written by Muhammad Habib ur Rehman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Technology & Engineering categories.
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.
Building Machine Learning Pipelines
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Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13
Building Machine Learning Pipelines written by Hannes Hapke and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-13 with Computers categories.
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
Handbook Of Python Navigating Ai And Machine Learning
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Author : Dr. Preeti Yadav
language : en
Publisher: GEH Press
Release Date : 2024-11-24
Handbook Of Python Navigating Ai And Machine Learning written by Dr. Preeti Yadav and has been published by GEH Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-24 with Computers categories.
In the vast landscape of programming languages, Python stands out as a versatile and powerful tool that has gained immense popularity in recent years. With its clean syntax, ease of use, and extensive libraries, Python has become the go-to choice for beginners and experienced developers alike. This chapter serves as a comprehensive introduction to the fundamental concepts and building blocks of Python programming.
Machine Learning With Python
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Author : Tarkeshwar Barua
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-09-03
Machine Learning With Python written by Tarkeshwar Barua and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-03 with Computers categories.
This book explains how to use the programming language Python to develop machine learning and deep learning tasks. It provides readers with a solid foundation in the fundamentals of machine learning algorithms and techniques. The book covers a wide range of topics, including data preprocessing, supervised and unsupervised learning, model evaluation, and deployment. By leveraging the power of Python, readers will gain the practical skills necessary to build and deploy effective machine learning models, making this book an invaluable resource for anyone interested in exploring the exciting world of artificial intelligence.
Knowledge Management And Acquisition For Intelligent Systems
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Author : Shiqing Wu
language : en
Publisher: Springer Nature
Release Date : 2024-11-15
Knowledge Management And Acquisition For Intelligent Systems written by Shiqing Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-15 with Computers categories.
This book constitutes the refereed proceedings of the 20th International Conference on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2024, held in Kyoto, Japan, during November 18–19, 2024. The 15 full papers and 9 short papers presented in this volume were carefully reviewed and selected from 52 submissions. These papers demonstrate advanced research on machine learning, natural language processing, computer vision, and intelligent systems.
Smart Trends In Computing And Communications
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Author : Yu-Dong Zhang
language : en
Publisher: Springer Nature
Release Date : 2021-10-25
Smart Trends In Computing And Communications written by Yu-Dong Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-25 with Technology & Engineering categories.
This book gathers high-quality papers presented at the Fifth International Conference on Smart Trends in Computing and Communications (SmartCom 2021), organized by Global Knowledge Research Foundation (GR Foundation) from March 2 – 3 , 2021. It covers the state of the art and emerging topics in information, computer communications, and effective strategies for their use in engineering and managerial applications. It also explores and discusses the latest technological advances in, and future directions for, information and knowledge computing and its applications.