Foundations of Libvirt Development by W. David Ashley

Foundations of Libvirt Development: How to Set Up and Maintain a Virtual Machine Environment with Python by W. David Ashley
Requirements: .PDF reader, 3 MB
Overview: Discover the essential concepts of libvirt development and see how to interface to Linux virtualization environments, such as QEMU/KVM, XEN, Virtuozzo, VMWare ESX, LXC, Bhyve, and more. This book will prepare you to set up and maintain a virtual machine environment. You’ll start by reviewing virtualization in general and then move on to libvirt-specific concepts using Python, including virtualized operating systems and networks, connections, storage pools, and event and error handling. This work concludes with a comprehensive look at the XML schema definitions for domains, networks, devices, network filtering, storage, node devices, and more. The libvirt API covers the entire life cycle of virtual objects, from creation to destruction. It contains everything needed for the management of a virtual object during that life cycle. While libvirt has APIs that support many languages, Foundations of Libvirt Development concentrates on Python exclusively, and how to use the APIs to control virtual machines under the QEMU/KVM system. and more.
Genre: Non-Fiction > Tech & Devices

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Foundations Of Psychological Testing: A Practical Approach

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Image Texture Analysis: Foundations, Models And Algorithms

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The Social Foundations Reader: Critical Essays On …

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Foundations Of Psychological Testing: A Practical Approach

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Foundations For Clinical Mental Health Counseling: An …

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Soils And Foundations (8th Edition)

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Foundations for Research by Kathleen B. deMarrais

Foundations for Research: Methods of Inquiry in Education and the Social Sciences by Kathleen B. deMarrais (Editor), Stephen D. Lapan
Requirements: .PDF reader, 3.7 MB
Overview: Designed for introductory research courses in the professional fields and social sciences, this text acquaints students and beginning researchers with a broad view of research methodologies and an understanding of the assumptions that inform each of these approaches. More experienced researchers will also find the book useful in acquainting them with methodologies and theoretical frameworks that are new to them.
Genre: Non-Fiction > Educational

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Lead Generation Foundations

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Logical Foundations of Cyber-Physical Systems by Andre Platzer

Logical Foundations of Cyber-Physical Systems by Andre Platzer
Requirements: .PDF reader, 10,2 MB
Overview: Cyber-physical systems (CPSs) combine cyber capabilities, such as computation or communication, with physical capabilities, such as motion or other physical processes. Cars, aircraft, and robots are prime examples, because they move physically in space in a way that is determined by discrete computerized control algorithms. Designing these algorithms is challenging due to their tight coupling with physical behavior, while it is vital that these algorithms be correct because we rely on them for safety-critical tasks. This textbook teaches undergraduate students the core principles behind CPSs. It shows them how to develop models and controls; identify safety specifications and critical properties; reason rigorously about CPS models; leverage multi-dynamical systems compositionality to tame CPS complexity; identify required control constraints; verify CPS models of appropriate scale in logic; and develop an intuition for operational effects.
Genre: Non-Fiction > Tech & Devices

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Business Analysis Foundations

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DevOps Foundations Going Cloud Native

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Jeet Kune Do Foundations by Jason Korol

Jeet Kune Do Foundations by Jason Korol
Requirements: .ePUB reader, 10 MB
Overview: It’s easy to see the reality of violence. It’s easy to see that it isn’t choreographed and that highly complex routines won’t work under the pressures of a real fight because bad guys don’t stand still and cooperate. Moreover, it’s easy to see that real fights aren’t sporting matches or sparring contests either. Yes, this is all obvious to the person who wants to learn a method in order to protect themselves against a real threat. What isn’t so easy is finding a method that takes all these things seriously and gives you the simple truth of the matter. That was, in fact, exactly what the late Bruce Lee sought when he developed his ingenious fighting method, Jeet Kune Do. His brilliance wasn’t only in his movies but also in the fact that he understood the root of the issue. And what’s that? Simple – that the whole goal is keeping yourself as safe as possible and the best defense is a good offense and the best offense is a scientific counter-attack. Thus, the heart of JKD is the intercepting/stopping fist (or foot). Most methods give you dozens – even hundreds – of techniques. JKD favors the straight punch and kick. The entire system is a set-up for the basics and once they’re understood and practiced properly they can be expertly deployed by anyone. In Jeet Kune Do Foundations you’ll find these basics and you’ll learn the underlying philosophy that sets Lee’s intercepting fist method apart.
Genre: Non-Fiction > Health, Fitness & Medical

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Foundations of Machine Learning, 2nd Edition by Mehryar Mohri

Foundations of Machine Learning, 2nd Edition by Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Requirements: .PDF reader, 10,2 MB
Overview: This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review.
Genre: Non-Fiction > Tech & Devices

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