Machine Learning And Ai In Network Monitoring A Complete Guide 2019 Edition
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Machine Learning And Ai In Network Monitoring A Complete Guide 2019 Edition
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Author : Gerardus Blokdyk
language : en
Publisher: 5starcooks
Release Date : 2019-06-22
Machine Learning And Ai In Network Monitoring A Complete Guide 2019 Edition written by Gerardus Blokdyk and has been published by 5starcooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-22 with categories.
What are your Machine Learning And Ai In Network Monitoring processes? How often will data be collected for measures? What new services of functionality will be implemented next with Machine Learning And Ai In Network Monitoring ? What happens when a new employee joins the organization? Why is Machine Learning And Ai In Network Monitoring important for you now? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Machine Learning And Ai In Network Monitoring investments work better. This Machine Learning And Ai In Network Monitoring All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Machine Learning And Ai In Network Monitoring Self-Assessment. Featuring 943 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Machine Learning And Ai In Network Monitoring improvements can be made. In using the questions you will be better able to: - diagnose Machine Learning And Ai In Network Monitoring projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Machine Learning And Ai In Network Monitoring and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Machine Learning And Ai In Network Monitoring Scorecard, you will develop a clear picture of which Machine Learning And Ai In Network Monitoring areas need attention. Your purchase includes access details to the Machine Learning And Ai In Network Monitoring self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Machine Learning And Ai In Network Monitoring Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Artificial Intelligence In Asset Management
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Author : Söhnke M. Bartram
language : en
Publisher: CFA Institute Research Foundation
Release Date : 2020-08-28
Artificial Intelligence In Asset Management written by Söhnke M. Bartram and has been published by CFA Institute Research Foundation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-28 with Business & Economics categories.
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Ai And Machine Learning For Network And Security Management
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Author : Yulei Wu
language : en
Publisher: John Wiley & Sons
Release Date : 2022-11-08
Ai And Machine Learning For Network And Security Management written by Yulei Wu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-08 with Computers categories.
AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.
Artificial Intelligence For Autonomous Networks
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Author : Mazin Gilbert
language : en
Publisher: CRC Press
Release Date : 2018-09-25
Artificial Intelligence For Autonomous Networks written by Mazin Gilbert and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-25 with Computers categories.
Artificial Intelligence for Autonomous Networks introduces the autonomous network by juxtaposing two unique technologies and communities: Networking and AI. The book reviews the technologies behind AI and software-defined network/network function virtualization, highlighting the exciting opportunities to integrate those two worlds. Outlining the new frontiers for autonomous networks, this book highlights their impact and benefits to consumers and enterprise customers. It also explores the potential of the autonomous network for transforming network operation, cyber security, enterprise services, 5G and IoT, infrastructure monitoring and traffic optimization, and finally, customer experience and care. With contributions from leading experts, this book will provide an invaluable resource for network engineers, software engineers, artificial intelligence, and machine learning researchers.
Applied Deep Learning
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Author : Dr. Rajkumar Tekchandani
language : en
Publisher: BPB Publications
Release Date : 2023-04-29
Applied Deep Learning written by Dr. Rajkumar Tekchandani and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-29 with Computers categories.
A comprehensive guide to Deep Learning for Beginners KEY FEATURES ● Learn how to design your own neural network efficiently. ● Learn how to build and train Recurrent Neural Networks (RNNs). ● Understand how encoding and decoding work in Deep Neural Networks. DESCRIPTION Deep Learning has become increasingly important due to the growing need to process and make sense of vast amounts of data in various fields. If you want to gain a deeper understanding of the techniques and implementations of deep learning, then this book is for you. The book presents you with a thorough introduction to AI and Machine learning, starting from the basics and progressing to a comprehensive coverage of Deep Learning with Python. You will be introduced to the intuition of Neural Networks and how to design and train them effectively. Moving on, you will learn how to use Convolutional Neural Networks for image recognition and other visual tasks. The book then focuses on localization and object detection, which are crucial tasks in many applications, including self-driving cars and robotics. You will also learn how to use Deep Learning algorithms to identify and locate objects in images and videos. In addition, you will gain knowledge on how to create and train Recurrent Neural Networks (RNNs), as well as explore more advanced variations of RNNs. Lastly, you will learn about Generative Adversarial Networks (GAN), which are used for tasks like image generation and style transfer. WHAT YOU WILL LEARN ● Learn how to work efficiently with various Convolutional models. ● Learn how to utilize the You Only Look Once (YOLO) framework for object detection and localization. ● Understand how to use Recurrent Neural Networks for Sequence Learning. ● Learn how to solve the vanishing gradient problem with LSTM. ● Distinguish between fake and real images using various Generative Adversarial Networks. WHO THIS BOOK IS FOR This book is intended for both current and aspiring Data Science and AI professionals, as well as students of engineering, computer applications, and masters programs interested in Deep learning. TABLE OF CONTENTS 1. Basics of Artificial Intelligence and Machine Learning 2. Introduction to Deep Learning with Python 3. Intuition of Neural Networks 4. Convolutional Neural Networks 5. Localization and Object Detection 6. Sequence Modeling in Neural Networks and Recurrent Neural Networks (RNN) 7. Gated Recurrent Unit, Long Short-Term Memory, and Siamese Networks 8. Generative Adversarial Networks
Machine Learning And Artificial Intelligence With Industrial Applications
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Author : Diego Carou
language : en
Publisher: Springer Nature
Release Date : 2022-03-11
Machine Learning And Artificial Intelligence With Industrial Applications written by Diego Carou and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-11 with Technology & Engineering categories.
This book presents the tools used in machine learning (ML) and the benefits of using such tools in facilities. It focus on real life business applications, explaining the most popular algorithms easily and clearly without the use of calculus or matrix/vector algebra. Replete with case studies, this book provides a working knowledge of ML current and future capabilities and the impact it will have on every business. It demonstrates that it is also possible to carry out successful ML and AI projects in any manufacturing plant, even without fully fulfilling the five V (Volume, Velocity, Variety, Veracity and Value) usually associated with big data. This book takes a closer look at how AI and ML are also able to work for industrial area, as well as how you could adapt some of the standard tips and techniques (usually for big data) for your own needs in your SME. Organizations which first understand these tools and know how to use them will benefit at the expense of their rivals.
Machine Learning A Complete Guide 2019 Edition
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Author : Gerardus Blokdyk
language : en
Publisher: 5starcooks
Release Date : 2018-12-21
Machine Learning A Complete Guide 2019 Edition written by Gerardus Blokdyk and has been published by 5starcooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-21 with categories.
What is the current state of the data? What is the relationship between different learning algorithms, and which should be used when? Can you use cloud-based data to train machine learning models? What rights should artificial beings have? How does knowledge lead to action? This powerful Machine Learning self-assessment will make you the credible Machine Learning domain auditor by revealing just what you need to know to be fluent and ready for any Machine Learning challenge. How do I reduce the effort in the Machine Learning work to be done to get problems solved? How can I ensure that plans of action include every Machine Learning task and that every Machine Learning outcome is in place? How will I save time investigating strategic and tactical options and ensuring Machine Learning costs are low? How can I deliver tailored Machine Learning advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Machine Learning essentials are covered, from every angle: the Machine Learning self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Machine Learning outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Machine Learning practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Machine Learning are maximized with professional results. Your purchase includes access details to the Machine Learning self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Machine Learning Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Azure Machine Learning Studio A Complete Guide 2019 Edition
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Author : Gerardus Blokdyk
language : en
Publisher: 5starcooks
Release Date : 2019-03-18
Azure Machine Learning Studio A Complete Guide 2019 Edition written by Gerardus Blokdyk and has been published by 5starcooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-18 with categories.
Is it necessary to have a simple quantitative metric to measure progress? To what degree can you have both data privacy and the benefits of data mining? How can machines improve with experience? Is it possible for health research to become a data-rich science? Are trainee-operators under the constant supervision of a skilled operator when learning to operate the machine? This instant Azure Machine Learning Studio self-assessment will make you the entrusted Azure Machine Learning Studio domain master by revealing just what you need to know to be fluent and ready for any Azure Machine Learning Studio challenge. How do I reduce the effort in the Azure Machine Learning Studio work to be done to get problems solved? How can I ensure that plans of action include every Azure Machine Learning Studio task and that every Azure Machine Learning Studio outcome is in place? How will I save time investigating strategic and tactical options and ensuring Azure Machine Learning Studio costs are low? How can I deliver tailored Azure Machine Learning Studio advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Azure Machine Learning Studio essentials are covered, from every angle: the Azure Machine Learning Studio self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Azure Machine Learning Studio outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Azure Machine Learning Studio practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Azure Machine Learning Studio are maximized with professional results. Your purchase includes access details to the Azure Machine Learning Studio self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Azure Machine Learning Studio Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Network Security Empowered By Artificial Intelligence
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Author : Yingying Chen
language : en
Publisher: Springer
Release Date : 2024-06-26
Network Security Empowered By Artificial Intelligence written by Yingying Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-26 with Computers categories.
This book introduces cutting-edge methods on security in spectrum management, mobile networks and next-generation wireless networks in the era of artificial intelligence (AI) and machine learning (ML). This book includes four parts: (a) Architecture Innovations and Security in 5G Networks, (b) Security in Artificial Intelligence-enabled Intrusion Detection Systems. (c) Attack and Defense in Artificial Intelligence-enabled Wireless Systems, (d) Security in Network-enabled Applications. The first part discusses the architectural innovations and security challenges of 5G networks, highlighting novel network structures and strategies to counter vulnerabilities. The second part provides a comprehensive analysis of intrusion detection systems and the pivotal role of AI and machine learning in defense and vulnerability assessment. The third part focuses on wireless systems, where deep learning is explored to enhance wireless communication security. The final part broadens the scope, examining the applications of these emerging technologies in network-enabled fields. The advancement of AI/ML has led to new opportunities for efficient tactical communication and network systems, but also new vulnerabilities. Along this direction, innovative AI-driven solutions, such as game-theoretic frameworks and zero-trust architectures are developed to strengthen defenses against sophisticated cyber threats. Adversarial training methods are adopted to augment this security further. Simultaneously, deep learning techniques are emerging as effective tools for securing wireless communications and improving intrusion detection systems. Additionally, distributed machine learning, exemplified by federated learning, is revolutionizing security model training. Moreover, the integration of AI into network security, especially in cyber-physical systems, demands careful consideration to ensure it aligns with the dynamics of these systems. This book is valuable for academics, researchers, and students in AI/ML, network security, and related fields. It serves as a resource for those in computer networks, AI, ML, and data science, and can be used as a reference or secondary textbook.
Developing Networks Using Artificial Intelligence
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Author : Haipeng Yao
language : en
Publisher: Springer
Release Date : 2019-06-16
Developing Networks Using Artificial Intelligence written by Haipeng Yao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-16 with Technology & Engineering categories.
This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine learning in network space. It also discusses the main challenge of network traffic intelligent awareness and introduces several machine learning-based traffic awareness algorithms, such as traffic classification, anomaly traffic identification and traffic prediction. The authors introduce some ML approaches like reinforcement learning to deal with network control problem in this book. Traditional works on the control plane largely rely on a manual process in configuring forwarding, which cannot be employed for today's network conditions. To address this issue, several artificial intelligence approaches for self-learning control strategies are introduced. In addition, resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. Compared with the traditional with-box approach, the authors present some ML methods to solve the complexity network resource allocation problems. Finally, semantic comprehension function is introduced to the network to understand the high-level business intent in this book. With Software-Defined Networking (SDN), Network Function Virtualization (NFV), 5th Generation Wireless Systems (5G) development, the global network is undergoing profound restructuring and transformation. However, with the improvement of the flexibility and scalability of the networks, as well as the ever-increasing complexity of networks, makes effective monitoring, overall control, and optimization of the network extremely difficult. Recently, adding intelligence to the control plane through AI&ML become a trend and a direction of network development This book's expected audience includes professors, researchers, scientists, practitioners, engineers, industry managers, and government research workers, who work in the fields of intelligent network. Advanced-level students studying computer science and electrical engineering will also find this book useful as a secondary textbook.