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data science --- artificial intelligence --- data analytics --- predictive modeling --- cybersecurity --- algorithms
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Die Beiträge dieses Sonderheftes befassen sich mit dem Trendthema „Big Data“ aus verschiedenen für Controller relevanten Blickwinkeln. Namhafte Wissenschaftler, Praktiker und Berater zeigen auf, wie Controller die neuen Möglichkeiten von Big Data für ihre eigene Arbeit nutzen und wie sie beurteilen können, ob sich Investitionen in diesen Bereich für ihr Unternehmen lohnen. Zudem wird beleuchtet, welche Auswirkungen Big Data auf die Aufgaben von Controller hat und welche Kompetenzen Controller aufbauen müssen, um große Datenmengen und unterschiedliche Informationsquellen für neue Anwendungsfelder zu nutzen.
Accounting. --- Bookkeeping . --- Big data. --- Accounting/Auditing. --- Big Data/Analytics.
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This book introduces the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. It is a workbook with MATLAB code that creates a common framework and points out various interconnections related to industry. This will allow the reader to connect previous subject knowledge to data science, machine learning, or analytics and apply it to IoT applications. Part One brings together subjects in machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two (Systems Analytics) develops a “universal” nonlinear, time-varying dynamical machine learning solution that can faithfully model all the essential complexities of real-life business problems and shows how to apply it. FEATURES:Develops a “universal,” nonlinear, dynamical machine learning solution to model and apply the complexities of modern applications in IoTCovers topics such as machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins.
IOT. --- MATLAB. --- computer science. --- data analytics. --- engineering. --- mathematics. --- physics.
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Computer science. --- Data analytics. --- Digital libraries and archives.
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Computer science. --- E-learning --- Data analytics --- Interactive learning environments
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This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA (“state of the art”). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included.FEATURES:Covers extensive topics related to natural language processing Includes separate appendices on regular expressions and probability/statisticsFeatures companion files with source code and figures from the book.The companion files are available online by emailing the publisher with proof of purchase at info@merclearning.com.
Computer Science. --- Data Analytics. --- Machine Learning. --- Natural Language Processing.
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Introduction -- Going on a tour with R -- Variability -- Machine learning -- Simulation -- Linear optimization -- Combinatorial optimization -- Simulation optimization --Dynamic programming and reinforcement learning -- Answers to exercises
Business enterprises / Decision making. --- Business mathematics. --- Data analytics.
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