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Unreal Engine Lighting and Rendering Essentials
by Muhammad A. MoniemLearn the principles of lighting and rendering in the Unreal Engine About This Book * Get acquainted with the concepts of lighting and rendering specific to Unreal * Use new features such as Realistic Rendering and Foliage Shading to breathe new life into your projects * A fast-paced guide to help you learn lighting and rendering concepts in Unreal Who This Book Is For This book is meant for game developers with knowledge of Unreal Engine and a basic understanding of lighting and rendering systems in it. As a prerequisite, you need to have good knowledge of C++. What You Will Learn * Use features such as realistic Rendering and Foliage Shading to create high quality output * Create and edit your materials using the Material Editor * Use Cascade's particle editor to create modular particle-based effects using emitters * Explore Unreal's GPU Visualizer * Tweak the overall look and feel of your scene with post-process effects * Create charts to get stat unit times over a long period of time * Use scalability settings to maintain performance for your games on different platforms and hardware In Detail Unreal Engine is a powerful game development engine that provides rich functionalities to create 2D and 3D games. Developers have the opportunity to build cross-platform mobile and desktop games from scratch. Unreal Engine enables users to create high quality games that focus on individual complexities of game development. This book provides you with the skills required to apply a high level of visual appeal to your games without compromising on performance. Starting with an introduction to the rendering system, you will learn to create different types of materials using the Material Editor. You will then create a particle system based on Cascade editor to create mind-blowing visual effects. Moving on, you will learn the concept of lights in Unreal and different types of dynamic/real-time lights, along with a number of powerful post processing effects. Next, you will learn to improve rendering performance, keeping in mind the rendering limitations for different platforms. At the end of the book, we will discuss the scalability settings menu, and how to add realistic fog effects based on the requirements of your game or level. Style and approach A fast-paced guide filled with hands-on examples to teach you the principles of lighting and rendering in Unreal.
Unreal Engine Physics Essentials
by Devin Sherry Katax EmperoreGain practical knowledge of mathematical and physics concepts in order to design and develop an awesome game world using Unreal Engine 4 About This Book * Use the Physics Asset Tool within Unreal Engine 4 to develop game physics objects for your game world * Explore the Collision mechanics within Unreal Engine 4 to create advanced, real-world physics * A step-by-step guide to implementing the Physics concepts involved in Unreal Engine 4 to create a working Vehicle Blueprint Who This Book Is For This book is intended for beginner to intermediate users of Epic Games' Unreal Engine 4 who want to learn more about how to implement physics within their game-world. No matter what your knowledge base of Unreal Engine 4 is, this book contains valuable information on blueprint scripting, collision generation, materials, and the Physical Asset Tool (PhAT) for all users to create better games. What You Will Learn * Get to know basic to intermediate topics in mathematics and physics * Create assets using the Physics Asset Tool (PhAT) in Unreal Engine 4 * Develop Collision Hulls, which are necessary to take advantage of Unreal Engine 4's physics and collision events * Use constraints to create advanced physics-based assets for your game-world * Working knowledge of physics bodies, physics damping, and friction within Unreal Engine 4 * Develop physical materials to recreate real-world friction for substances such as glass and ice * Create a working vehicle blueprint from scratch using assets provided by Unreal Engine 4 * Gain knowledge about implementing advanced physics in Unreal Engine 4 using C++ programming In Detail Unreal Engine 4 is one of the leading game development tools used by both AAA and independent developers alike to create breathe-taking games. One of the key features of this tool is the use of Physics to create a believable game-world for players to explore. This book gives readers practical insight into the mathematical and physics principles necessary to properly implement physics within Unreal Engine 4. Discover how to manipulate physics within Unreal Engine 4 by learning basic real-world mathematical and physics concepts that assist in the implementation of physics-based objects in your game world. Then, you'll be introduced to PhAT (Physics Asset Tool) within Unreal Engine 4 to learn more about developing game physics objects for your game world. Next, dive into Unreal Engine 4's collision generation, physical materials, blueprints, constraints, and more to get hands-on experience with the tools provided by Epic to create real-world physics in Unreal Engine 4. Lastly, you will create a working Vehicle Blueprint that uses all the concepts covered in this book, as well as covering advanced physics-based topics. Style and approach An easy-to-follow reference text filled with working examples of physics within Unreal Engine 4. Each topic is broken down to easily explain how to implement physics and physical objects in your game-world using the tools provided by Epic Games Unreal Engine 4.
Unreal Engine Virtual Reality Quick Start Guide: Design and Develop immersive virtual reality experiences with Unreal Engine 4
by Jessica PlowmanUnreal Engine 4 for virtual reality game design, development, User Experience design techniques and Blueprint programming to create virtual reality gameplay for HTC Vive, Oculus Rift, PSVR, and Windows Mixed Reality headsets. Key FeaturesBuild VR games from scratch with the power of Unreal Engine 4Learn User Experience design practices to take your VR game to the next levelUnderstand the best practices to creating art for games on HTC Vive, Oculus Rift, and PSVRBook DescriptionWith the ability to put players directly in the game, virtual reality gives users the chance to experience digital worlds directly. Nevertheless, many designers are unsure where to start when working with this amazing technology.With this book, you will learn user experience design processes and create immersive gameplay experiences designed for entertainment and player comfort. Using the power of Unreal Engine 4’s Blueprint visual scripting language, you will build player interaction and locomotion systems from scratch and use these flexible systems to create a sample game, as well as develop functional 2D and 3D user interfaces for players to interact with. And also learn the best practices for creating game art for virtual reality. Finally, you will learn how to test your application with your target audience and finalize your game for distribution.By the end of this book, you will have the knowledge to be able to make the leap from traditional game development to creating immersive virtual reality experiences using Unreal Engine 4.What you will learnUnderstand how to get started with VR development in Unreal Engine 4Design and create interaction and locomotion systems from scratchPlan and program a sample game for VRUnderstand how VR affects user experience and user interfacesDiscuss what is needed to create optimized art for VRTest your game with users and prepare for distributionWho this book is forThe audience for this book is intermediate or advanced users of Unreal Engine 4 but who have not begun working with VR technology. These users are familiar with the game engine and have an interest in VR technology. They are just beginning to explore the VR features that the game engine has to offer.
Unreal for Mobile and Standalone VR: Create Professional VR Apps Without Coding
by Cornel HillmannApply the techniques needed to build VR applications for mobile and standalone head-mounted displays (HMDs) using the Unreal Engine. This book covers the entire VR ecosystem including production tools, Unreal engine, workflows, performance and optimization, and presents two fully-developed projects to reinforce what you've learned. Media designers, CG artists and other creatives will be able to take advantage of real-time engine techniques and easy-to-learn visual scripting logic to turn their creations into immersive and interactive VR worlds.Gear VR, the Oculus Go and other Android based VR HMDs are becoming exciting new platforms for immersive business presentations, entertainment and educational solutions. The Unreal engine, one of the world’s most powerful and popular game engines, is now free to use and has become increasingly popular for real-time visualizations and enterprise solutions in recent years.With Unreal's powerful blueprint visual scripting system, non-coders can now design blueprints in Unreal, unlock the power of rapid prototyping, and create complex interactions without a line of code. Get your copy of Unreal for Mobile and Standalone VR today and begin using this powerful tool-set to create high-end VR apps for a wide range of applications from games, B2B, to education.What You'll LearnExplore the VR ecosystem, including history, recent trends and future outlookReview tool set, graphics and animation pipeline (Blender, Zbrush, Substance Painter and others)Examine graphics optimization techniquesSet up a project and the target platformDesign interaction with Unreal blueprintsDeployments, testing, further optimizationWho This Book Is ForMultimedia designers, CG artists, producers, app developers. No coding experience is required.
Unreal Game Development
by Ashish Amresh Alex OkitaUsing Unreal Engine 3, the authors teach aspiring game makers the fundamentals of designing a computer game. The only prerequisite is a basic working knowledge of computers and a desire to build an original game.This book mirrors the curriculum used at CampGame, a six week summer program organized for high school students at The New York University and Arizona State University. Students enter with no prior knowledge of game making, and through the course of six intensive weeks, they finish as teams of budding game developers.
UnrealScript Game Programming Cookbook
by Dave VoylesFilled with a practical collection of recipes, the UnrealScript Game Programming Cookbook is full of clear step-by-step instructions that help you harness the powerful scripting language to supplement and add AAA quality to your very own projects.This essential Cookbook has been assembled with both the hobbyist and professional developer in mind. A solid foundation of object oriented programming knowledge will be required. All examples can be replicated and used by UDK and in some cases other software and tools - all of which are available for free - can be used too.
Unsere digitale Zukunft
by Carsten KönnekerDroht die ferngesteuerte Gesellschaft?Dieses Buch greift das weithin diskutierte, zum Jahreswechsel 2015/16 veröffentlichte „Digital-Manifest“ auf und führt die Debatte entlang vielfältiger Themenlinien weiter. Es geht hierbei um nicht weniger als unsere – digitale – Zukunft: Welche Chancen eröffnen künstliche Intelligenz und digitale Technologien, welche Risiken und ethische Herausforderungen bergen sie? Wie schützen wir unsere Daten und die Privatsphäre? Wie sichern wir individuelle Freiheit und Demokratie vor Gefahren der digitalen Verhaltenssteuerung? Wie sollen selbstfahrende Autos, Roboter und autonome Agenten unser Leben prägen? Als Gesellschaft und als Individuen müssen wir uns mit verschiedenen Projektionen in die Zukunft auseinandersetzen. Dabei sollten wir die Einschätzungen führender Experten in der Zusammenschau vernehmen und diskutieren. Den kritischen Dialog zu beflügeln, ist das Ziel dieses Sammelbands mit den wichtigsten Beiträgen namhafter Wissenschaftler aus Spektrum der Wissenschaft, Spektrum – Die Woche und Spektrum.de.
Unsteady Computational Fluid Dynamics in Aeronautics
by P. G. TuckerThe field of Large Eddy Simulation (LES) and hybrids is a vibrant research area. This book runs through all the potential unsteady modelling fidelity ranges, from low-order to LES. The latter is probably the highest fidelity for practical aerospace systems modelling. Cutting edge new frontiers are defined. One example of a pressing environmental concern is noise. For the accurate prediction of this, unsteady modelling is needed. Hence computational aeroacoustics is explored. It is also emerging that there is a critical need for coupled simulations. Hence, this area is also considered and the tensions of utilizing such simulations with the already expensive LES. This work has relevance to the general field of CFD and LES and to a wide variety of non-aerospace aerodynamic systems (e.g. cars, submarines, ships, electronics, buildings). Topics treated include unsteady flow techniques; LES and hybrids; general numerical methods; computational aeroacoustics; computational aeroelasticity; coupled simulations and turbulence and its modelling (LES, RANS, transition, VLES, URANS). The volume concludes by pointing forward to future horizons and in particular the industrial use of LES. The writing style is accessible and useful to both academics and industrial practitioners. From the reviews: "Tucker's volume provides a very welcome, concise discussion of current capabilities for simulating and modellng unsteady aerodynamic flows. It covers the various possible numerical techniques in good, clear detail and presents a very wide range of practical applications; beautifully illustrated in many cases. This book thus provides a valuable text for practicing engineers, a rich source of background information for students and those new to this area of Research & Development, and an excellent state-of-the-art review for others. A great achievement." Mark Savill FHEA, FRAeS, C.Eng, Professor of Computational Aerodynamics Design & Head of Power & Propulsion Sciences, Department of Power & Propulsion, School of Engineering, Cranfield University, Bedfordshire, U.K. "This is a very useful book with a wide coverage of many aspects in unsteady aerodynamics method development and applications for internal and external flows." L. He, Rolls-Royce/RAEng Chair of Computational Aerothermal Engineering, Oxford University, U.K. "This comprehensive book ranges from classical concepts in both numerical methods and turbulence modelling approaches for the beginner to latest state-of-the-art for the advanced practitioner and constitutes an extremely valuable contribution to the specific Computational Fluid Dynamics literature in Aeronautics. Student and expert alike will benefit greatly by reading it from cover to cover." Sébastien Deck, Onera, Meudon, France
The Unstoppable Sales Machine: How to Connect, Convert, and Close New Customers
by Shawn CasemoreThis book addresses a gap in how organizations adopt and introduce modern sales strategies. It is written for business owners, sales executives, leaders, and professionals -- anyone who has the desire to create a rapid and sustained increase in their sales, without having to invest a significant amount of time or money in doing so. This book, a comprehensive review of the author's work with clients, introduces "Unstoppable Selling" -- it captures the strategies and tactics the author's clients have used to allow them predictability in their sales. All of the powerful models, tools, and resources are contained here, including the Unstoppable Sales Strategic Multiplier, Hybrid Sales Funnel, Velocity Stack, and Customer Empowerment Service Model. In addition, the book demonstrates how you can quickly establish your Unstoppable Sales Machine regardless of the size or sector of your business. Installing your own unstoppable sales machine will not require you to hire a bunch of experts or more employees. This book accepts you where you are and then walks you through the steps to quickly introduce and launch your very own machine. You’ll find all of the advice, guidance, case studies, and worksheets contained in this one convenient book, ready for you to implement. If you intend to scale your business, or you simply want more freedom from the daily rollercoaster of your current sales strategy, then this is the book for you. The author wrote this book because sales is a noble profession and is the heart and soul of every business -- Yet the continued evolution of today’s customers, how they engage, select and buy products and services, requires we rethink how we approach selling. He shows you how to become an expert at sales while having the freedom and comfort in knowing that your machine will never let you down.
The Unstoppable Sales Team: Elevate Your Team’s Performance, Win More Business, and Attract Top Performers
by Shawn CasemoreWhy are companies like Salesforce, Whirlpool, and Cintas repeatedly recognized for their top sales performance? What are they doing that sets them apart from their competition, allowing them to increase sales revenue year over year? It’s not a result of their ability to master online sales funnels or introduce software that automates their sales process. Instead, these companies dominate in their markets because they continually elevate their sales team’s performance to the level of being unstoppable. This book is written for sales executives, sales leaders, and sales managers. If you lead a sales team and want to accelerate their performance without being forced to invest in new technology, hire more employees or completely restructure your existing sales team, then this book is for you. The Unstoppable Sales Team contains the lessons learned, best practices and observations applied through the author’s work with sales teams globally. Building on his popular book The Unstoppable Sales Machine, the author shares the best strategies for building a high-performing sales team that outsells and outperforms their competition.
Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention
by Jean Paul IssonTurn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.
Unsupervised: Navigating and Influencing a World Controlled by Powerful New Technologies
by Daniel Doll-Steinberg Stuart LeafHow a broad range of new immensely powerful technologies is disrupting and transforming every corner of our reality—and why you must act and adapt Unsupervised: Navigating and Influencing a World Controlled by Powerful New Technologies examines the fast-emerging technologies and tools that are already starting to completely revolutionize our world. Beyond that, the book takes an in-depth look at how we have arrived at this dizzying point in our history, who holds the reins of these formidable technologies, mostly without any supervision. It explains why we as business leaders, entrepreneurs, academics, educators, lawmakers, investors or users and all responsible citizens must act now to influence and help oversee the future of a technological world. Quantum computing, artificial intelligence, blockchain, decentralization, virtual and augmented reality, and permanent connectivity are just a few of the technologies and trends considered, but the book delves much deeper, too. You’ll find a thorough analysis of energy and medical technologies, as well as cogent predictions for how new tech will redefine your work, your money, your entertainment, your transportation and your home and cities, and what you need to know to harness and prosper from these technologies. Authors Daniel Doll-Steinberg and Stuart Leaf draw on their decades of building and implementing disruptive technologies, investing and deploying funds, and advising business leaders, governments and supranational bodies on change management, the future of work, innovation and disruption, education and the economy to consider how every area of our lives, society, economy and government will likely witness incredible changes in the coming decade. When we look just a bit further into the future, we can see that the task facing us is to completely reinvent life as we know it—work, resources, war, and even humanity itself will undergo redefinition, thanks to these new and emerging tools. In Unsupervised, you’ll consider what these redefinitions might look like, and how we as individuals, and part of society, can prevent powerful new technologies from falling into the wrong hands or be built to harm us. Get a primer on the foundational technologies that are reshaping business, pleasure, and life as we know it Learn about the lesser known, yet astonishing, technologies set to revolutionize medicine, agriculture, and beyond Consider the potential impact of new tech across business sectors—and what it means for you Gain the knowledge and inspiration you need to harness your own power and push the future in the direction of good for all of us not just the few Explore the best ways to invest in the changes these technologies of the future will bring about This is a remarkably thorough and comprehensive look at the future of technology and everything it touches. Shining a light on many unsupervised technologies and their unsupervised oligarchy of masters.
Unsupervised Classification
by Sanghamitra Bandyopadhyay Sriparna SahaClustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
Unsupervised Domain Adaptation: Recent Advances and Future Perspectives (Machine Learning: Foundations, Methodologies, and Applications)
by Jingjing Li Lei Zhu Zhekai DuUnsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicability in various real-world scenarios. This book provides a comprehensive review of state-of-the-art UDA methods and explores new variants of UDA that have the potential to advance the field. The book begins with a clear introduction to the UDA problem and is mainly organized into four technical sections, each focused on a specific piece of UDA research. The first section covers criterion optimization-based UDA, which aims to learn domain-invariant representations by minimizing the discrepancy between source and target domains. The second section discusses bi-classifier adversarial learning-based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents active learning for UDA, which combines domain adaptation and active learning to reduce the amount of labeled data needed for adaptation. This book is suitable for researchers, graduate students, and practitioners who are interested in UDA and its applications in various fields, primarily in computer vision. The chapters are authored by leading experts in the field and provide a comprehensive and in-depth analysis of the current UDA methods and new directions for future research. With its broad coverage and cutting-edge research, this book is a valuable resource for anyone looking to advance their knowledge of UDA.
Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
by Y-h. TaguchiThis book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features;Provides a fast algorithm, based upon linear algebra, to analyze big data;Includes several applications to multi-view data analyses, with a focus on bioinformatics.
Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
by Y-h. TaguchiThis updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
Unsupervised Information Extraction by Text Segmentation
by Eli Cortez Altigran S. SilvaA new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors' approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a number of results are produced to address the IETS problem in an unsupervised fashion. In particular, the authors develop, implement and evaluate distinct IETS methods, namely ONDUX, JUDIE and iForm. ONDUX (On Demand Unsupervised Information Extraction) is an unsupervised probabilistic approach for IETS that relies on content-based features to bootstrap the learning of structure-based features. JUDIE (Joint Unsupervised Structure Discovery and Information Extraction) aims at automatically extracting several semi-structured data records in the form of continuous text and having no explicit delimiters between them. In comparison with other IETS methods, including ONDUX, JUDIE faces a task considerably harder that is, extracting information while simultaneously uncovering the underlying structure of the implicit records containing it. iForm applies the authors' approach to the task of Web form filling. It aims at extracting segments from a data-rich text given as input and associating these segments with fields from a target Web form. All of these methods were evaluated considering different experimental datasets, which are used to perform a large set of experiments in order to validate the presented approach and methods. These experiments indicate that the proposed approach yields high quality results when compared to state-of-the-art approaches and that it is able to properly support IETS methods in a number of real applications. The findings will prove valuable to practitioners in helping them to understand the current state-of-the-art in unsupervised information extraction techniques, as well as to graduate and undergraduate students of web data management.
Unsupervised Learning: A Dynamic Approach (IEEE Press Series on Computational Intelligence)
by Matthew Kyan Paisarn Muneesawang Kambiz Jarrah Ling GuanA new approach to unsupervised learning Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge—for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers. Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data—from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data. Self-organization concepts and applications discussed include: Distance metrics for unsupervised clustering Synaptic self-amplification and competition Image retrieval Impulse noise removal Microbiological image analysis Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.
Unsupervised Learning Algorithms
by M. Emre Celebi Kemal AydinThis book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
by B.K. Tripathy Anveshrithaa Sundareswaran Shrusti GhelaUnsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. FEATURES Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples, and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.
Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks (Advances in Computer Vision and Pattern Recognition)
by Marius LeordeanuThis book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Unsupervised Learning with R
by Erik Rodriguez PachecoWork with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data About This Book * Unlock and discover how to tackle clusters of raw data through practical examples in R * Explore your data and create your own models from scratch * Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide Who This Book Is For This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement. What You Will Learn * Load, manipulate, and explore your data in R using techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization * Transform your data by using approaches such as scaling, re-centering, scale [0-1], median/MAD, natural log, and imputation data * Build and interpret clustering models using K-Means algorithms in R * Build and interpret clustering models by Hierarchical Clustering Algorithm's in R * Understand and apply dimensionality reduction techniques * Create and use learning association rules models, such as recommendation algorithms * Use and learn about the techniques of feature selection * Install and use end-user tools as an alternative to programming directly in the R console In Detail The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning. If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console. Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques. By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects. Style and approach This book takes a step-by-step approach to unsupervised learning concepts and tools, explained in a conversational and easy-to-follow style. Each topic is explained sequentially, explaining the theory and then putting it into practice by using specialized R packages for each topic.
The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
by Benjamin Johnston Aaron Jones Christopher KrugerLearning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities Key Features Get familiar with the ecosystem of unsupervised algorithms Learn interesting methods to simplify large amounts of unorganized data Tackle real-world challenges, such as estimating the population density of a geographical area Book Description Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights. What you will learn Distinguish between hierarchical clustering and the k-means algorithm Understand the process of finding clusters in data Grasp interesting techniques to reduce the size of data Use autoencoders to decode data Extract text from a large collection of documents using topic modeling Create a bag-of-words model using the CountVectorizer Who this book is for If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.
Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles (AutoUni – Schriftenreihe #159)
by Fabian Kai NoeringIn the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles.
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
by Chris Aldrich Lidia AuretThis unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.