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Pro Unity Game Development with C#
by Alan ThornIn Pro Unity Game Development with C#, Alan Thorn, author of Learn Unity for 2D Game Development and experienced game developer, takes you through the complete C# workflow for developing a cross-platform first person shooter in Unity. C# is the most popular programming language for experienced Unity developers, helping them get the most out of what Unity offers. If you're already using C# with Unity and you want to take the next step in becoming an experienced, professional-level game developer, this is the book you need. Whether you are a student, an indie developer, or a season game dev professional, you'll find helpful C# examples of how to build intelligent enemies, create event systems and GUIs, develop save-game states, and lots more. You'll understand and apply powerful programming concepts such as singleton classes, component based design, resolution independence, delegates, and event driven programming. By the end of the book, you will have a complete first person shooter game up and running with Unity. Plus you'll be equipped with the know-how and techniques needed to deploy your own professional-grade C# games. If you already know a bit of C# and you want to improve your Unity skills, this is just the right book for you. What you'll learnHow to plan your game in terms of C# and classes How to import assets from Blender and Maya, including C# automation processes How to handle events and notifications using a C# event notification system How to create intelligent enemies and collectible weapons How to build a cross-platform controller as well as how to write platform-specific code How to develop a resolution-independent graphical user interfaceWho this book is for If you already know a bit of C# and you want to improve your Unity skills, this isjust the right book for you. Unity developers looking to improve their C# workflow and effectiveness, including game development students and professionals, indie developers, artists, designers, and those employed at game development studios. Table of Contents1. Outlining the Game Structurein Terms of C# 2. Optimizing Import Workflows and Import Settings 3: The Game Loop and Developiong a Custom Event-Handling System 4. Building a Cross-Platform Controller 5. Enemies, NPCs, and Artificial Intelligence 6. Custom Weapons: Targeting, Feedback, and More 7. Modifying Game Behavior with Power-ups and Collections 8. The Graphical User Interface and Resolution Independence 9. Persistent Data and Save Game States 10. Final Touches: Polishing and Play-testing"
Pro Vim
by Mark McdonnellPro Vim teaches you the real-world workflows, tips, and tricks of this powerful, terminal-based text editor. This book covers all the essentials, as well as lesser-known but equally powerful features that will ensure you become a top-level performant and professional user, able to jump between multiple sessions while manipulating and controlling with ease many different documents and programming files. With easy-to-digest chapters on all the areas you need to learn, this book is a key addition to your library that will enable you to become a fast, efficient user of Vim. Using this book, you will learn how to properly configure your terminal environment and work without even touching the mouse. You will become an expert in how Vim actually works: how buffers and sessions work, automation through Macros and shell scripting, real-world workflows, and how to work efficiently and fast with plugins and different themes. You will also learn practical, real-world tips on how to best utilize Vim alongside the terminal multiplexer tmux; helping you to manage files across multiple servers and terminal sessions. Avoid common pitfalls and work with best practice ways to efficiently edit and control your files and sessions from the terminal interface. Vim is an advanced power tool that is commonly recognized as being difficult to learn, even for experienced developers. This book shows you how to become an expert by focusing on not only the fundamentals of how Vim works, but also by distilling the author's own experiences learning Vim into an easy-to-understand and follow guide. It's time to bring your programming, editing, and workflow skills up to the professional level - use Pro Vim today. What you'll learn * Understand the fundamentals of how Vim works so you can better utilize its features. * Extend Vim using plugins; along with specific plugins that cover a wide range of technical requirements. * Automate Vim and tmux via the use of Macros and Scripting. * Learn how to make complex pattern based changes across multiple Vim buffers at once. * Pair program with remote users connecting to a single local tmux session. * Learn real-world workflows that integrate both Vim and tmux together. Who this book is for Pro Vim is for any developer who wishes (or has tried in the past and failed) to understand how to leverage the tools provided by Vim and tmux and integrate them into their professional working environment. Allowing them to take advantage of the power features these applications provide to become a better programmer. Table of Contents 1. Introduction 2. Installation and Configuration 3. Fundamentals 4. Files 5. Commands 6. Registers 7. Folding 8. Visual Block Mode 9. Bulk Command Processing 10. Editing Workflow 11. Search and Replace 12. Buffer/Window/Tab Management 13. Automation 14. Lists 15. Marks 16. Sessions 17. Plugins 18. Diffing 19. Custom Commands and Bindings 20. Terminal integration 21. Working with code 22. Practical Tips and Tricks 23. Terminal Multiplexer 24. Fundamentals 25. Modifications 26. Copy and Paste 27. Scripting and Automation 28. Pane/Window Management 29. Pair Programming 30. Workflow Management
Pro Windows 8.1 Development with XAML and C#
by Jesse Liberty Philip Japikse Jon GallowayWindows 8. 1 apps are revolutionizing development on the Windows platform. Fast, fluid, tactile and chrome-free, they provide a brand-new look and feel for Windows users. These apps rely on Microsoft's Windows 8 modern UI to provide their rich and engaging user experiences for both desktop and tablet users. The new UI in turn relies upon the Windows Runtime (WinRT) to give its apps unparalleled flexibility and power. Understanding this stack of new technologies and how they tie in to the proven C# language and the XAML standard is the subject of this book. Experienced writers Jesse Liberty, Phil Japikse, and Jon Galloway explain how you can get the most from Windows 8. 1 by focusing on the features that you need for your project and bringing your existing C# coding knowledge to bear. They begin with a nuts-and-bolts examination of how the technologies fit together and show you everything you need to get up and running with the new platform. Once you have a good understanding on the basics, you progress to more advanced topics steadily increasing your understanding as a whole. This holistic knowledge is essential to truly master Windows 8. 1 development. Each topic is covered clearly and concisely and is packed with the details you need to code effectively. The most important features are given a no-nonsense, in-depth treatment and chapters contain examples that demonstrate both the power and the subtlety of Windows 8. 1. What you'll learnWhat Metro and WinRT are capable of and why they are special Ways to use advanced features to create immersive and engaging Windows 8. 1 applications How to create applications that work seamlessly on tablets and desktops How to prepare and deploy your Windows 8. 1 applications Who this book is for This book is suitable for anyone wanting to get to grips with Windows 8. 1 development using the cross-platform standards of XAML and C#. Table of Contents1 Getting Started 2 Building Your First Windows 8 App 3 Themes, Panels, and Controls4 Binding5 Views6 Local Data7 Remote Data and Services8 Search and Share Contracts9 Notifications10 Application Life Cycle11 Making Money12 Publishing Your App"
Pro Windows Phone App Development
by Falafel SoftwareThe Windows Phone 8 platform provides a remarkable opportunity for Windows developers to create state-of-the-art mobile applications using their existing skills and a familiar toolset. Pro Windows Phone App Development, Third Edition, helps you unlock the potential of this platform and create dazzling, visually rich, and highly functional applications for the Windows Phone Store and bring you up to speed on the new features the Windows Phone 8 API provides. For developers new to the Windows Phone platform--whether with . NET, iOS, or Android experience--this book starts by introducing the requirements, specifications, and basics of Windows Phone development, and then leads you through the complete application development process, using an array of complementary technologies and Microsoft's modern-style app design. Along the way, you'll learn how to Use Microsoft technologies like XAML, . NET, Visual Studio 2012, and Expression Blend effectively to develop modern-style Windows Phone apps Take advantage of the device's sensors with the location service, accelerometer, and touch Make your apps location-aware using GPS data Develop rich media applications that harness the graphics capabilities of Windows Phone models Design and develop Windows Phone applications using the Model-View-ViewModel architecture Publish and sell your application through the Windows Phone Store Whether you're a Microsoft developer, an iOS or Android developer, or someone with prior Windows Phone experience, Pro Windows Phone App Development, Third Edition, is an ideal guide for mastering the Windows Phone 8 platform and compelling Windows Phone app development. What you'll learn How to use Microsoft technologies like XAML, . NET, Visual Studio 2012, and Expression Blend effectively to develop modern-style Windows Phone apps Techniques for taking advantage of the device's sensors with the location service, accelerometer, and touch How to make your apps location-aware using GPS data How to develop rich media applications that harness the graphics capabilities of Windows Phone models The way to design and develop Windows Phone applications using the Model-View-ViewModel architecture The process for publishing and selling your application through the Windows Phone Store Who this book is for If you're a Microsoft developer, this book is primarily for you--you're eager to learn how to use your existing skills to develop for the new Windows Phone platform. If you're an iOS or Android developer, this is an ideal guide for you to learn how to expand the market for your existing applications. This does assume some knowledge of C#, managed code in general, and a basic level of familiarity with Visual Studio. And if you're a proficient Windows Phone developer, get up to speed quickly with the new API endpoints and HTML5 browser support in the Tango update. Software developers proficient in other languages will also find this book helpful to get up to speed with developing Windows Phone applications. Table of Contents Introduction Getting Started WinRT and XAML Phone Controls Navigation Application Life Cycling Gestures Device Support Mapping Live Tiles & Notifications Data Sources Using Data in the Cloud Designing in Expression Blend Marketing Your Applications
Pro Windows Subsystem for Linux (WSL): Powerful Tools and Practices for Cross-Platform Development and Collaboration
by Hayden BarnesThis book covers everything a developer needs to know to hit the ground running and get the most out of Windows Subsystem for Linux (WSL). Since its release, Windows Subsystem for Linux (WSL) has been growing in popularity, moving from curious early adopters to wide-scale interest, including enterprise development teams using WSL in production. This authoritative guide to WSL covers the gamut, introducing developers to WSL architecture, installation and configuration, the WSL command line, all the way to advanced use cases and performance tunings. Practical examples are sprinkled throughout to reinforce understanding. This book is designed to efficiently and effectively get developers comfortable using this highly useful platform for open-source development on Windows. WSL is uniquely suited to cloud and cross-platform development, and system administrator workflows on Windows. Windows developers will begin with the basics of installation and then be introduced to the vast library of open source tools that they can integrate into their own workflows, using their existing development tools, such as Code, Visual Studio, and JetBrains IDEs. Readers will learn, hands on, about using WSL to develop cross-platform and cloud-native applications, work with containers, and deploy a local Kubernetes cluster on WSL. “Much of what WSL is, is what developers make of it” is expert Barnes’ guiding mantra, a theme that is reinforced throughout this valuable cross-platform learning journey. Developers will get excited about the many new opportunities at their fingertips and be astounded at what they can do and achieve with WSL. What You Will Learn Install and configure WSL, a unique and novel configuration processReceive an unbiased overview of WSL, its architecture, installation, the command line, practical use cases, and advanced configurationCreate a development workstation using WSLCompare and contrast the differences between WSL 1 and WSL 2Explore, in depth, some of the more popular workflows in WSL, including Docker containersConsider and plan key factors for a large scale enterprise deployment of WSL Who This Book Is ForDevelopers who need to know WSL and how to build a development stack, integrating it with their preferred code editor or IDE if they so choose; existing Windows and Linux system administrators who want to learn how to install, deploy, and manage WSL; power users who are comfortable in a command line, but may be new to Linux or WSL
Pro WordPress: Mastering the Techniques for Building, Securing and Scaling Websites
by Sivaraj SelvarajPro WordPress is your ultimate guide to unlocking the full potential of the world's leading content management system. From novice bloggers to seasoned developers, this comprehensive resource offers a step-by-step journey through every aspect of WordPress customization, security and performance optimization. With clear explanations and practical examples, you'll learn how to set up your WordPress environment, choose the right themes and plugins, and customize your site with advanced techniques such as custom post types, widgets, shortcodes, and more. Dive deep into the world of WordPress security and discover how to safeguard your website against cyber threats with strategies like two-factor authentication, secure file permissions, and regular security audits. But that's not all – this book also equips you with the tools and knowledge to optimize your site for lightning-fast performance and high search engine rankings. Learn how to leverage caching mechanisms, minimize HTTP requests, and implement SEO strategies to boost your site's speed and visibility. Whether you're managing a single WordPress site or overseeing a multisite network, you'll find invaluable insights and best practices for scalability and high availability. Real-world case studies provide inspiration and guidance, showcasing successful WordPress implementations and effective strategies for growth. Whether you're a business owner, freelancer, or aspiring web developer, Pro WordPress empowers you to take control of your online presence and build websites that stand out in today's competitive digital landscape. Unlock the full potential of WordPress and elevate your web development skills with this essential resource. You Will Learn: The WordPress ecosystem in its entirety, including its history, core features, and community dynamics. Develop expertise in customizing WordPress themes and plugins using CSS, PHP, and advanced techniques like custom post types and widgets Implement robust security measures to protect your WordPress site from common vulnerabilities, such as brute force attacks and malicious code injections Optimize your website's performance through caching mechanisms, image optimization, and other techniques to enhance user experience and SEO rankings More advanced topics such as managing multisite networks, scalability and high availability to effectively scale your WordPress projects and handle high traffic volumes Who is it for: Web designers and developers to business owners looking to develop a webiste of their own as well as bloggers and hobbyists who are looking to design, launch and maintain a website whatever the project.
Pro WordPress Theme Development
by Adam OnishiPro WordPress Theme Development is your comprehensive guide to creating advanced WordPress themes. Designed for for professional web designers and developers who are comfortable with PHP and WordPress, this book teaches you every aspect of professional theme development. You will learn how to build themes from scratch, how to monetize the themes you create, and how to capitalize on this by creating advanced themes for your clients or selling premium themes. This book builds on your current knowledge of PHP and web development to create a WordPress theme from scratch. It uses a real-world theme example that you can build, to demonstrate each feature in a practical way. It shows you how to take control of WordPress with custom posts types and taxonomies, and covers anatomy and hierarchy, use of the loop, hooks, short codes, plug-ins and much more. WordPress is one of the most successful open-source blogging and content management systems available, and theme development has become a major part of the WordPress ecosystem. Start working with WordPress themes like a pro today with Pro WordPress Theme Development. What you'll learn How to create a WordPress theme from scratch How to use the WordPress system to your advantage to create amazing advanced functionality How to earn money through selling your custom themes How to take control of WordPress as a content management system with custom posts types and taxonomies How you should secure your WordPress theme to give peace of mind to your user Who this book is for Pro WordPress Theme Development is for web designers and developers who want to start creating their own themes and get the most out of them. This book is for web professionals who are familiar with PHP and WordPress, and have used both before, but want to go from editing themes to creating their own custom themes. Pro WordPress Theme Development is perfect for developers who want to create themes from scratch with advanced features, capitalize on the large WordPress community, and monetize their new found skills. Table of Contents Getting Started Theme Anatomy and Template Hierarchy Content Options and The Loop Using Custom Post Types Creating Custom Taxonomies and Fields Customize with Hooks and Short Codes Theme Options Users, Roles, and Permissions Plugins - When The Time is Right Security and Performance Distributing Your WordPress Theme Extending your WordPress Theme Plugin Development WordPress Multisite
Pro XAML with C#
by Buddy James Lori LalondePro XAML with C#: Application Development Strategies is your guide to real-world development practices on Microsoft's XAML-based platforms, with examples in WPF, Windows 8. 1, and Windows Phone 8. 1. Learn how to properly plan and architect an application on one or more of these platforms for a robust, scalable solution. In Part I, authors Buddy James and Lori Lalonde introduce you to XAML and reveal proven techniques for developing successful line-of-business applications. You'll also find out about some of the conflicting needs and interests that you might encounter as an enterprise XAML developer. Part II begins to lay the groundwork to help you properly architect your application, providing you with a deeper understanding of domain-driven design and the Model-View-ViewModel design pattern. You will also learn about proper exception handling and logging techniques, and how to cover your code with unit tests to reduce bugs and validate your design. Part III explores implementation and deployment details for each of Microsoft's XAML UIs, along with advice on deploying and maintaining your application across different devices using version control repositories and continuous integration. Pro XAML with C# Application Development Strategies is for intermediate to experienced developers looking to improve their professional practice. Readers should have experience working with C# and at least one XAML-based technology (WPF, Silverlight, Windows Store, or Windows Phone). What you'll learn Analyze a business problem and develop a solution within the sometimes conflicting interests of a real business team Use domain driven design to get maximum business value from your development efforts Develop applications in Visual Studio making best use of its integrated design and development views Implement the popular MVVM design pattern to decouple your user interface from your core domain logic Cover your code with unit tests to reduce bugs and validate your design Deploy and maintain your application across different devices Who this book is for Pro XAML with C#: Application Development Strategies is for intermediate to experienced . NET developers. Readers should have experience working with C# and at least one XAML-based technology (WPF, Silverlight, Windows Store or Windows Phone). Table of Contents Part I: Getting Started Chapter 1: What Is XAML? Chapter 2: Software Craftsmanship Part II: Laying The Groundwork Chapter 3: Domain-Driven Design Chapter 4: Design Patterns Chapter 5: Unit Testing Chapter 6: Advanced Unit Testing and Test-Driven Development Chapter 7: Exception Handling and Logging Part III: Completing the User Interface Layer Chapter 8: The WPF User Interface Chapter 9: The Windows Phone User Interface Chapter 10: The Windows User Interface Chapter 11: Deploying and Maintaining Your Application
The PROACT® Root Cause Analysis: Quick Reference Guide (Reliability, Maintenance, and Safety Engineering)
by Kenneth C. Latino Mark A. Latino Robert J. LatinoRoot Cause Analysis, or RCA, "What is it?" Everyone uses the term, but everyone does it differently. How can we have any uniformity in our approach, much less accurately compare our results, if we’re applying different definitions? At a high level, we will explain the difference between RCA and Shallow Cause Analysis, because that is the difference between allowing a failure to recur or dramatically reducing the risk of recurrence.In this book, we will get down to basics about RCA, the fundamentals of blocking and tackling, and explain the common steps of any investigative occupation. Common investigation steps include: Preserving evidence (data)/not allowing hearsay to fly as fact Organizing an appropriate team/minimizing potential bias Analyzing the events/reconstructing the incident based on actual evidence Communicating findings and recommendations/ensuring effective recommendations are actually developed and implemented Tracking bottom-line results/ensuring that identified, meaningful metrics were attained We explore, "Why don’t things always go as planned?" When our actual plans deviate from our intended plans, we usually experience some type of undesirable or unintended outcome. We analyze the anatomy of a failure (undesirable outcome) and provide a step-by-step guide to conducting a comprehensive RCA based on our 3+ decades of applying RCA as we have successfully practiced it in the field. This book is written as a how-to guide to effectively apply the PROACT® RCA methodology to any undesirable outcome, is directed at practitioners who have to do the real work, focuses on the core elements of any investigation, and provides a field-proven case as a model for effective application. This book is for anyone charged with having a thorough understanding of why something went wrong, such as those in EH&S, maintenance, reliability, quality, engineering, and operations to name just a few.
Proactive and Dynamic Network Defense (Advances in Information Security #74)
by Cliff Wang Zhuo LuThis book discusses and summarizes current research issues, identifies challenges, and outlines future directions for proactive and dynamic network defense. This book also presents the latest fundamental research results toward understanding proactive and dynamic network defense by top researchers in related areas. It includes research results that offer formal frameworks to define proactive and dynamic network defense, and develop novel models to analyze and evaluate proactive designs and strategies in computer systems, network systems, cyber-physical systems and wireless networks. A wide variety of scientific techniques have been highlighted to study these problems in the fundamental domain. As the convergence of our physical and digital worlds grows fast pace, protecting information systems from being tampered or unauthorized access is becoming one of the most importance issues. The traditional mechanisms of network defense are built upon a static, passive, and reactive nature, which has insufficient to defend against today's attackers that attempt to persistently analyze, probe, circumvent or fool such mechanisms. It has not yet been fully investigated to address the early stage of “cyber kill chain” when adversaries carry out sophisticated reconnaissance to plan attacks against a defense system. Recently, proactive and dynamic network defense has been proposed as an important alternative towards comprehensive network defense. Two representative types of such defense are moving target defense (MTD) and deception-based techniques. These emerging approaches show great promise to proactively disrupt the cyber-attack kill chain and are increasingly gaining interest within both academia and industry. However, these approaches are still in their preliminary design stage. Despite the promising potential, there are research issues yet to be solved regarding the effectiveness, efficiency, costs and usability of such approaches. In addition, it is also necessary to identify future research directions and challenges, which is an essential step towards fully embracing proactive and dynamic network defense. This book will serve as a great introduction for advanced-level computer science and engineering students who would like to start R&D efforts in the field of proactive and dynamic network defense. Researchers and professionals who work in this related field will also find this book useful as a reference.
Proactive Data Mining with Decision Trees
by Haim Dahan Shahar Cohen Lior Rokach Oded MaimonThis book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students.
Probabilistic Cellular Automata: Theory, Applications And Future Perspectives (Emergence, Complexity And Computation Ser. #27)
by Pierre-Yves Louis Francesca R. NardiThis book explores Probabilistic Cellular Automata (PCA) from the perspectives of statistical mechanics, probability theory, computational biology and computer science. PCA are extensions of the well-known Cellular Automata models of complex systems, characterized by random updating rules. Thanks to their probabilistic component, PCA offer flexible computing tools for complex numerical constructions, and realistic simulation tools for phenomena driven by interactions among a large number of neighboring structures. PCA are currently being used in various fields, ranging from pure probability to the social sciences and including a wealth of scientific and technological applications. This situation has produced a highly diversified pool of theoreticians, developers and practitioners whose interaction is highly desirable but can be hampered by differences in jargon and focus. This book – just as the workshop on which it is based – is an attempt to overcome these difference and foster interest among newcomers and interaction between practitioners from different fields. It is not intended as a treatise, but rather as a gentle introduction to the role and relevance of PCA technology, illustrated with a number of applications in probability, statistical mechanics, computer science, the natural sciences and dynamical systems. As such, it will be of interest to students and non-specialists looking to enter the field and to explore its challenges and open issues.
Probabilistic Data Structures for Blockchain-Based Internet of Things Applications
by Neeraj Kumar Arzoo MiglaniThis book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the applicability of PDS in BC to technology practitioners and explains each PDS through code snippets and illustrative examples. Further, it provides references for the applications of PDS to BC along with implementation codes in python language for various PDS so that the readers can gain confidence using hands on experience. Organized into five sections, the book covers IoT technology, fundamental concepts of BC, PDS and algorithms used to estimate membership query, cardinality, similarity and frequency, usage of PDS in BC based IoT and so forth.
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability
by Beate Sick Oliver DuerrProbabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you&’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. About the book Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
by Daphne Koller Nir FriedmanA general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Probabilistic Graphical Models
by Luis Enrique SucarThis accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.
Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
by Luis Enrique SucarThis fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, graphical models, and deep learning, as well as an even greater number of exercises.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features:Presents a unified framework encompassing all of the main classes of PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructorsThis classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.
Probabilistic Group Theory, Combinatorics, and Computing
by Alla Detinko Dane Flannery Eamonn O'BrienProbabilistic Group Theory, Combinatorics and Computing is based on lecture courses held at the Fifth de Brún Workshop in Galway, Ireland in April 2011. Each course discusses computational and algorithmic aspects that have recently emerged at the interface of group theory and combinatorics, with a strong focus on probabilistic methods and results. The courses served as a forum for devising new strategic approaches and for discussing the main open problems to be solved in the further development of each area. The book represents a valuable resource for advanced lecture courses. Researchers at all levels are introduced to the main methods and the state-of-the-art, leading up to the very latest developments. One primary aim of the book's approach and design is to enable postgraduate students to make immediate use of the material presented.
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
by Kevin P. MurphyA detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author&’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
by Kevin P. MurphyAn advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment
Probabilistic Machine Learning for Civil Engineers
by James-A. GouletAn introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises.This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws.The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment.
Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python
by Deepak K. KanungoThere are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.
Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots (Cognitive Systems Monographs #40)
by Tomasz Piotr Kucner Achim J. Lilienthal Martin Magnusson Luigi Palmieri Chittaranjan Srinivas SwaminathanThis book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.The world around us is constantly changing. Nonetheless, we can find our way and aren’t overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field.
Probabilistic Methods and Distributed Information: Rudolf Ahlswede’s Lectures on Information Theory 5 (Foundations in Signal Processing, Communications and Networking #15)
by Rudolf Ahlswede Alexander Ahlswede Ingo Althöfer Christian Deppe Vladimir Blinovsky Ulrich Tamm Holger Boche Ulrich Krengel Ahmed MansourThe fifth volume of Rudolf Ahlswede’s lectures on Information Theory focuses on several problems that were at the heart of a lot of his research. One of the highlights of the entire lecture note series is surely Part I of this volume on arbitrarily varying channels (AVC), a subject in which Ahlswede was probably the world's leading expert. Appended to Part I is a survey by Holger Boche and Ahmed Mansour on recent results concerning AVC and arbitrarily varying wiretap channels (AVWC). After a short Part II on continuous data compression, Part III, the longest part of the book, is devoted to distributed information. This Part includes discussions on a variety of related topics; among them let us emphasize two which are famously associated with Ahlswede: "multiple descriptions", on which he produced some of the best research worldwide, and "network coding", which had Ahlswede among the authors of its pioneering paper. The final Part IV on "Statistical Inference under Communication constraints" is mainly based on Ahlswede’s joint paper with Imre Csiszar, which received the Best Paper Award of the IEEE Information Theory Society. The lectures presented in this work, which consists of 10 volumes, are suitable for graduate students in Mathematics, and also for those working in Theoretical Computer Science, Physics, and Electrical Engineering with a background in basic Mathematics. The lectures can be used either as the basis for courses or to supplement them in many ways. Ph.D. students will also find research problems, often with conjectures, that offer potential subjects for a thesis. More advanced researchers may find questions which form the basis of entire research programs.
Probabilistic Topic Models: Foundation and Application
by Di Jiang Chen Zhang Yuanfeng SongThis book introduces readers to the theoretical foundation and application of topic models. It provides readers with efficient means to learn about the technical principles underlying topic models. More concretely, it covers topics such as fundamental concepts, topic model structures, approximate inference algorithms, and a range of methods used to create high-quality topic models. In addition, this book illustrates the applications of topic models applied in real-world scenarios. Readers will be instructed on the means to select and apply suitable models for specific real-world tasks, providing this book with greater use for the industry. Finally, the book presents a catalog of the most important topic models from the literature over the past decades, which can be referenced and indexed by researchers and engineers in related fields. We hope this book can bridge the gap between academic research and industrial application and help topic models play an increasingly effective role in both academia and industry. This book offers a valuable reference guide for senior undergraduate students, graduate students, and researchers, covering the latest advances in topic models, and for industrial practitioners, sharing state-of-the-art solutions for topic-related applications. The book can also serve as a reference for job seekers preparing for interviews.