Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis introduces the theory and latest applications of transfer learning on rotary machine fault diagnosis and prognosis. Transfer learning-based rotary machine fault diagnosis is a relatively new subject, and this innovative book synthesizes recent advances from academia and industry to provide systematic guidance. Basic principles are described before key questions are answered, including the applicability of transfer learning to rotary machine fault diagnosis and prognosis, technical details of models, and an introduction to deep transfer learning. Case studies for every method are provided, helping readers apply the techniques described in their own work. Key Features: Offers case studies for each transfer learning algorithm. Optimizes the transfer learning models to solve specific engineering problems. Describes the roles of transfer components, transfer fields,and transfer order in intelligent machine diagnosis and prognosis."--Provided by publisher.

Transfer learning (Machine learning) --- Fault location (Engineering) --- Machinery. --- Data processing.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Machine learning --- Apprentissage automatique --- Machine learning. --- Apprentissage automatique. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Machine Learning.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Machine learning. --- Algorithms. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Algorism --- Algebra --- Arithmetic --- Foundations

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how e...

Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Machine learning --- E-books

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning concepts and techniques applied to key areas within optical communications and networking, reflecting the state-of-the-art research and industrial practices. The book gives knowledge and insights into the role machine learning-based mechanisms will soon play in the future realization of intelligent optical network infrastructures that can manage and monitor themselves, diagnose and resolve problems, and provide intelligent and efficient services to the end users.

Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Optical fiber communication.