Machine Learning And Optimization Models For Optimization In Cloud
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Machine Learning And Optimization Models For Optimization In Cloud
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Author : Punit Gupta
language : en
Publisher: CRC Press
Release Date : 2022-02-27
Machine Learning And Optimization Models For Optimization In Cloud written by Punit Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-27 with Computers categories.
Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.
Machine Learning And Optimization Models For Optimization In Cloud
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Author : Punit Gupta
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2022
Machine Learning And Optimization Models For Optimization In Cloud written by Punit Gupta and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Computers categories.
Machine Learning and Models for Optimization in Cloud's main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.
Optimization For Machine Learning
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Author : Suvrit Sra
language : en
Publisher: MIT Press
Release Date : 2012
Optimization For Machine Learning written by Suvrit Sra and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Automated Machine Learning
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Author : Adnan Masood
language : en
Publisher: Packt Publishing
Release Date : 2021-02-18
Automated Machine Learning written by Adnan Masood and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-18 with categories.
Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key Features: Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice Eliminate mundane tasks in data engineering and reduce human errors in machine learning models Find out how you can make machine learning accessible for all users to promote decentralized processes Book Description: Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What You Will Learn: Explore AutoML fundamentals, underlying methods, and techniques Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario Find out the difference between cloud and operations support systems (OSS) Implement AutoML in enterprise cloud to deploy ML models and pipelines Build explainable AutoML pipelines with transparency Understand automated feature engineering and time series forecasting Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems Who this book is for: Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
Machine Learning For Cloud Management
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Author : Jitendra Kumar
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2021-11-25
Machine Learning For Cloud Management written by Jitendra Kumar and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-25 with Computers categories.
Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. Machine learning enabled solutions are the best fit to address these issues as they can analyze and learn from the data. Moreover, it brings automation to the solutions, which is an essential factor in dealing with large distributed systems in the cloud paradigm. Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The predictive approaches are developed using regression-based time series analysis and neural network models. The neural network-based models are primarily trained using evolutionary algorithms, and efficient virtual machine placement schemes are developed using multi-objective genetic algorithms. Key Features: The first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds. Predictive analytics is an integral part of efficient cloud resource management, and this book gives a future research direction to researchers in this domain. It is written by leading international researchers. The book is ideal for researchers who are working in the domain of cloud computing.
Deep Learning Models On Cloud Platforms
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Author : Vijay Ramamoorthi
language : en
Publisher: RK Publication
Release Date : 2024-07-25
Deep Learning Models On Cloud Platforms written by Vijay Ramamoorthi and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-25 with Computers categories.
Deep Learning Models on Cloud Platforms provides an in-depth exploration of the integration of deep learning techniques with cloud computing environments. Architectures, and frameworks for developing and deploying deep learning models at scale. It addresses practical considerations, including data management, computational resources, and cost-efficiency, while highlighting popular cloud platforms like AWS, Google Cloud, and Azure. Through real-world examples and case studies, readers will gain insights into best practices for leveraging cloud infrastructure to enhance deep learning capabilities and drive innovation across various industries.
Computer Applications In Engineering And Management
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Author : Parveen Berwal
language : en
Publisher: CRC Press
Release Date : 2022-04-08
Computer Applications In Engineering And Management written by Parveen Berwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-08 with Computers categories.
The book Computer Applications in Engineering and Management is about computer applications in management, electrical engineering, electronics engineering, and civil engineering. It covers the software tools for office automation, introduces the basic concepts of database management, and provides an overview about the concepts of data communication, internet, and e-commerce. Additionally, the book explains the principles of computing management used in construction of buildings in civil engineering and the role of computers in power grid automation in electronics engineering. Features Provides an insight to prospective research and application areas related to industry and technology Includes industry-based inputs Provides a hands-on approach for readers of the book to practice and assimilate learning This book is primarily aimed at undergraduates and graduates in computer science, information technology, civil engineering, electronics and electrical engineering, management, academicians, and research scholars.
Building Machine Learning And Deep Learning Models On Google Cloud Platform
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Author : Ekaba Bisong
language : en
Publisher:
Release Date : 2019
Building Machine Learning And Deep Learning Models On Google Cloud Platform written by Ekaba Bisong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Cloud computing categories.
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. You will: Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results Know the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products.
Optimization Models
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Author : Giuseppe C. Calafiore
language : en
Publisher: Cambridge University Press
Release Date : 2014-10-31
Optimization Models written by Giuseppe C. Calafiore 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 2014-10-31 with Business & Economics categories.
This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.
The 2021 International Conference On Machine Learning And Big Data Analytics For Iot Security And Privacy
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Author : John Macintyre
language : en
Publisher: Springer Nature
Release Date : 2021-11-02
The 2021 International Conference On Machine Learning And Big Data Analytics For Iot Security And Privacy written by John Macintyre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-02 with Computers categories.
This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.