Matematisk datateori

Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey Features:Implement state-of-the-art graph neural network architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description:Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What You Will Learn:Understand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is for:This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

> > With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers. > How can decision makers use data and technology to ensure desired outcomes? How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments? How can organizational decision makers assess and improve their decisions over time?

The fusion of the Internet of Things (IoT) with the prowess of Machine Learning and Deep Learning has ignited a revolution in the realm of technology. "IoT Application of Machine Learning and Deep Learning" is a comprehensive journey into this transformative synergy, designed to illuminate the convergence of these cutting-edge domains. This book aims to be a guiding light for enthusiasts, practitioners, and scholars navigating the dynamic landscape where IoT's interconnected devices meet the intelligence of machine and deep learning algorithms.In these pages, readers will embark on an expedition through the myriad applications, breakthroughs, and evolving possibilities of leveraging machine and deep learning within the IoT ecosystem. From unraveling the intricate mechanisms behind predictive analytics in IoT to exploring the robustness of neural networks in enhancing IoT security, this book unveils the intricate tapestry of possibilities awaiting exploration. Whether you're a seasoned professional seeking deeper insights or an eager learner venturing into this interdisciplinary realm, this book endeavors to be a beacon, illuminating the boundless potential at the nexus of IoT, Machine Learning, and Deep Learning.

Welcome to the world of AI and Deep Learning! This book is a culmination of years of research, innovation, and practical insights into the fascinating realms of artificial intelligence and deep learning. In this rapidly evolving landscape, the convergence of neuroscience, mathematics, and computer science has sparked a revolution, redefining how machines perceive, learn, and interact with the world. As you embark on this educational journey, you'll delve into the foundational principles that underpin these technologies, unraveling complex concepts in a lucid and accessible manner. Whether you're an aspiring data scientist, a curious enthusiast, or a seasoned professional seeking to expand your knowledge, this book aims to equip you with a solid understanding of AI and deep learning essentials, empowering you to navigate the frontiers of innovation and contribute meaningfully to this transformative field.In this comprehensive guide, we traverse the landscape of artificial intelligence and deep learning, demystifying intricate theories and methodologies. From the fundamentals of neural networks to the practical applications in image recognition, natural language processing, and beyond, each chapter is meticulously crafted to provide a holistic view of these cutting-edge technologies. With a blend of theoretical foundations, real-world examples, and hands-on exercises, this book is designed to foster a deep comprehension of the core concepts while igniting your creativity to explore and innovate. Join us on this immersive journey, where you'll not only grasp the essence of AI and deep learning but also cultivate the skills to create intelligent systems that can revolutionize industries, redefine possibilities, and shape the future.

¿This book constitutes the proceedings of the 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023, as well as the Cardiac MRI Reconstruction Challenge, CMRxRecon Challenge. There was a total of 53 submissions to the workshop. The 24 regular workshop papers included in this volume were carefully reviewed and selected from 29 paper submissions. They deal with cardiac segmentation, modelling, strain quantification, registration, statistical shape analysis, and quality control. In addition, 21 papers from the CMRxRecon challenge are included in this volume. They focus on fast CMR image reconstruction and provide a benchmark dataset that enables the broader research community to promote advances in this area of research.

This book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques.The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on ¿social infrastructure¿ applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases.The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.

This is the first book that sums up test-related modeling of digital circuits and systems by a new structural-decision-diagrams model. The model represents structural and functional information jointly and opens a new area of research.The book introduces and discusses applications of two types of structural decision diagrams (DDs): low-level, structurally synthesized binary DDs (SSBDDs) and high-level DDs (HLDDs) that enable diagnostic modeling of complex digital circuits and systems.Topics and features:Provides the definition, properties and techniques for synthesis, compression and optimization of SSBDDs and HLDDsProvides numerous working examples that illustrate the key points of the textDescribes applications of SSBDDs and HLDDs for various electronic design automation (EDA) tasks, such as logic-level fault modeling and simulation, multi-valued simulation, timing-critical path identification, and test generationDiscusses the advantages of the proposed model to traditional binary decision diagrams and other traditional design representationsCombines SSBDDs with HLDDs for multi-level representation of digital systems for enabling hierarchical and cross-level solving of complex test-related tasksThis unique book is aimed at researchers working in the fields of computer science and computer engineering, focusing on test, diagnosis and dependability of digital systems. It can also serve as a reference for graduate- and advanced undergraduate-level computer engineering and electronics courses.Three authors are affiliated with the Dept. of Computer Systems at the Tallinn University of Technology, Estonia: Raimund Ubar is a retired Professor, Jaan Raik and Maksim Jenihhin are tenured Professors. Artur Jutman, PhD, is a researcher at the same university and the CEO of Testonica Lab Ltd., Estonia.

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applicationsKey FeaturesWork with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithmsEvaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoringUncover effective approaches to deep learning for computer vision, time series analysis, and forecastingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDiscover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications.By navigating the versatile machine learning tools in the MATLAB environment, you'll learn how to seamlessly interact with the workspace. You'll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you'll explore various classification and regression techniques, skillfully applying them with MATLAB functions.This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You'll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you'll leverage MATLAB tools for deep learning and managing convolutional neural networks.By the end of the book, you'll be able to put it all together by applying major machine learning algorithms in real-world scenarios.What you will learnDiscover different ways to transform data into valuable insightsExplore the different types of regression techniquesGrasp the basics of classification through Naive Bayes and decision treesUse clustering to group data based on similarity measuresPerform data fitting, pattern recognition, and cluster analysisImplement feature selection and extraction for dimensionality reductionHarness MATLAB tools for deep learning explorationWho this book is forThis book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.Table of ContentsExploring MATLAB for Machine LearningWorking with Data in MATLABPrediction Using Classification and RegressionClustering Analysis and Dimensionality ReductionIntroducing Artificial Neural Networks ModelingDeep Learning and Convolutional Neural NetworksNatural Language Processing Using MATLABMATLAB for Image Processing and Computer VisionTime Series Analysis and Forecasting with MATLABMATLAB Tools for Recommender SystemsAnomaly Detection in MATLAB

Petri nets model concurrent and distributed systems where active components communicate through the production and absorption of various kinds of resources. Although the dynamic properties of such systems may be very complex, they may sometimes be connected to the static structure of a Petri net. Many properties are decidable, but their complexity may be huge. It is often opportune to restrict oneself to classes of systems, to partial algorithms, and to similar but simpler properties. Instead of analysing a given system, it is also possible to search for a system satisfying some desired properties by construction. This comprehensive textbook/reference presents and discusses these issues in-depth in the context of one of the most fundamental Petri net models, called place/transition nets. The presentation is fortified by means of many examples and worked exercises. Among topics addressed: ¿ In which order may actions may be generated and scheduled? ¿ What states and configurations may be reached in a concurrent system? ¿ Which interesting classes of systems can be analysed relatively efficiently? ¿ Is it possible to synthesise a system of some class from its behaviour? ¿ How can systems be represented algebraically, compositionally, and concisely? This unique text, based on introductory as well as on advanced courses on distributed systems, will serve as an invaluable guide for students and (future) researchers interested in theoretical¿as well as in practical¿aspects of Petri nets and related system models. Eike Best has been a full professor (now retired) affiliated to Carl von Ossietzky Universität Oldenburg, Germany. Raymond Devillers has been a full professor (now retired) affiliated to Université Libre de Bruxelles, Belgium. The authors have a long record as collaborators in the fields of Petri nets and the semantics of concurrency.

This conference LNAI 14502 volume constitutes the workshop proceedings of 22nd Mexican International Conference on Artificial Intelligence, held in November 2023 in Mérida, Yucatán, México. The total of 34 papers presented in this volume was carefully reviewed and selected from 54 submissions.The proceedings of MICAI 2023 workshops are structured into three sections:¿ WILE 2023: 16th Workshop on Intelligent Learning Environments¿ HIS 2023: 16th Workshop of Hybrid Intelligent Systems¿ CIAPP 2023: 5th Workshop on New Trends in Computational Intelligence and Applications

Edge computing has been identified as one of the key technologies for 5G networks and beyond due to two prominent advantages: low network latency and reduced core network load. By empowering cloud capabilities and IT service environments at the network edge, edge computing can well support applications of 5G and beyond, such as augmented/virtual reality (AR/VR), vehicular network (ultra-reliable low-latency communication services)¿Internet of Things (massive machine type communication services), and mobile high-definition video (enhanced mobile broadband services). Therefore, edge computing has attracted the attention of both industry and academia since its emergence.This book highlights the progress of 5G edge computing in both industry and academia according to our long-term efforts, including the current practice of public edge providers, the research process of edge computing from academia, the integration of edge computing with 5G, and the future visions of edge computing in the 6G era. From this book, the readers can benefit from: (1) the first comprehensive measurement study on a leading public edge platform, NEP (next-generation edge platform); 2) a clear and in-depth introduction of the key technologies of 5G edge computing; (3) the latest progress of 5G-integrated edge computing; and (4) pioneering exploration of 6G edge computing based on Tiansuan constellation ¿ an open satellite-terrestrial integrated platform. Both the researchers from academia or practitioners from industry can benefit significantly from this book.

Mathematik für die Informatik: Dieses Buch bringt Ihnen die Grundlagen beiDieses Lehrbuch bietet eine Einführung in die grundlegenden Begriffe und Strukturen der Mathematik, welche am Anfang eines Informatikstudiums relevant sind. Weiterhin demonstriert es Anwendungen von mathematischen Konzepten und Methoden in der Informatik. Diese betreffen insbesondere formale Methoden der Programmverifikation und -entwicklung und den Entwurf von generischen Programmen. Ein spezielles Konzept mit einer leicht verständlichen Vermittlung des Stoffs, vielen Beispielen mit Rückgriffen auf die Schulmathematik und detaillierten Beweisen (verbunden mit der Erklärung des logischen Hintergrunds) erleichtert den Einstieg in die Mathematik an einer wissenschaftlichen Hochschule. Dadurch werden die Studierenden auch auf spätere Begriffe und tiefergehende Anwendungen der Mathematik in der Informatik gut vorbereitet. Die 5. Auflage erweitert den Inhalt um die Grundlagen der linearen Algebra. Die Übungsaufgaben zu den einzelnen Kapitel helfen, das Erlernte zu festigen und zu kontrollieren. Zahlreiche Lösungsvorschläge am Ende des Buchs ermöglichen die Überprüfung der eigenen Lösungen.Der InhaltMengentheoretische Grundlagen ¿ Logische Grundlagen ¿ Allgemeine direkte Produkte und Datenstrukturen ¿ Mathematische Beweise ¿ Spezifikation und Programmverifikation ¿ Spezielle Funktionen ¿ Spezielle Relationen und gerichtete Graphen ¿ Elementare Kombinatorik und ungerichtete Graphen ¿ Diskrete Wahrscheinlichkeitstheorie ¿ Generische Programmierung ¿ Grundbegriffe algebraischer Strukturen ¿ Formale Einführung der natürlichen Zahlen - Grundbegriffe der linearen AlgebraMit diesem Buch gelingt der Einstieg ins Informatik-StudiumMit diesem Buch schaffen Sie eine solide Basis für die Mathematikausbildung im Rahmen desInformatikstudiums. Zudem sind Sie durch die vorgestellten Problemstellungen in der Lage, selbstständig mathematische Konzepte und Methoden anzuwenden. Zielgruppen dieses Buchs über Mathematik in der Informatik sind Bachelor-Studierende in den ersten Studiensemestern folgender Fachbereiche:InformatikMathematikIngenieurwissenschaften

This book constitutes the refereed proceedings of the 19th International Conference on Formal Aspects of Component Software, FACS 2023, which took place virtually during October 19-20, 2023.The 11 full papers included in this book were carefully reviewed and selected from 23 submissions. They were organized in topical sections as follows: cloud computing, cyber-physical and critical systems, and the Internet of Things.

This book constitutes the revised selected papers of the 5th International Workshop on Dynamic Logic. New Trends and Applications, DaLí 2023, held in Tbilisi, Georgia, during September 15¿16, 2023. The 8 full papers in this book were carefully reviewed and selected from 10 submissions. They deal with new trends and applications in the area of Dynamic Logic.

This book constitutes the refereed proceedings of the 26th International Conference on Practical Aspects of Declarative Languages, PADL 2024, held in London, UK, during January 17¿19, 2024.The 13 full papers included in this book were carefully reviewed and selected from 25 submissions. The accepted papers span a range of topics related to functional and logic programming, including reactive programming, hardware implementations, implementation of marketplaces, query languages, and applications of declarative programming techniques to artificial intelligence and machine learning.