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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process;
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Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis;
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Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for;
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deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets Who This Book Is For This book;
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experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques;
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Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing;
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the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to;
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the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to;
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> Covers advanced problems and solutions of smart grids using advance data analytic techniques;
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If you are an active user of statistics to examine trends in your business, then using machine learning is your next best step. It uses;
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any online entitlements included with the product. INCREASE your odds of learning STATISTICAL process control (SPC) Identify and;
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an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced;
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architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical;
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REPL * Apply standard and advanced machine learning techniques using Scala * Get acquainted with Big Data technologies and grasp why we need;
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In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow;
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and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics;
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processing and machine learning as key approaches to quantitative finance. * Offers advanced mathematical tools for high-dimensional portfolio;
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Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation;
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informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.;
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classical and advanced versions of mechanistic model based state estimation algorithms Describes various data-driven model based state;
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The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning;
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, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.;
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This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour;
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data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R;
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Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining;
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Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining;
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is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn;
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