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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

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

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis;

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Introduction to Deep Learning Using R

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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Julia for Data Science

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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Mastering Azure Machine Learning

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

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing;

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Conversational AI

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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Conversational AI

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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Intelligent Data-Analytics for Condition Monitoring

> Covers advanced problems and solutions of smart grids using advance data analytic techniques;

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A Simple Guide to Data Driven Technologies using Machine Learning and Deep Learning

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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Statistical Process Control Demystified

any online entitlements included with the product. INCREASE your odds of learning STATISTICAL process control (SPC) Identify and;

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Bioconductor Case Studies

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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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical;

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Mastering Scala Machine Learning

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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Towards Adaptive Spoken Dialog Systems

In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow;

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Deep Learning for Data Analytics

and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics;

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Financial Signal Processing and Machine Learning

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

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation;

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Statistical Data Analytics

informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.;

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Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

classical and advanced versions of mechanistic model based state estimation algorithms Describes various data-driven model based state;

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Statistics and Machine Learning Methods for EHR Data

The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning;

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Data-Driven Fault Detection and Reasoning for Industrial Monitoring

, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.;

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Advances in Applications of Data Driven Computing

This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour;

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Advanced R Statistical Programming and Data Models

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

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

Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining;

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Beginning Anomaly Detection Using Python-Based Deep Learning

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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