Deep Learning In Engineering Energy And Finance
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Deep Learning in Engineering, Energy and Finance
Unlock the transformative potential of deep learning in your professional and academic endeavors with "Deep Learning in Engineering, Energy and Finance: Principals and Applications." This comprehensive guide seamlessly bridges the gap between theoretical concepts and practical implementations, providing you with the knowledge and tools to revolutionize industries and drive innovation. Delve into real-world applications and cutting-edge research that showcase how deep learning is redefining engineering processes, optimizing energy systems, and reshaping financial markets. This book: Explores deep learning applications across engineering, energy, and finance, highlighting diverse use cases and industry-specific challenges. Discover how deep learning is driving breakthroughs in predictive maintenance, energy optimization, algorithmic trading, and risk management. Illustrates all the concepts connected to Deep Learning from head and heart with real-time practical examples and case studies. Stresses on skills needed to tackle future challenges, with a focus on emerging deep learning technologies oriented towards Solar Energy, SOM's, Stock Market, Speech Technology and Many more. Whether you're a student eager to explore the latest advancements or a seasoned R&D professional seeking to enhance your skill set, this book offers invaluable insights and practical guidance to elevate your expertise.
Deep Learning in Engineering, Energy and Finance
Unlock the transformative potential of deep learning in your professional and academic endeavors with Deep Learning in Engineering, Energy and Finance: Principals and Applications. This comprehensive guide seamlessly bridges the gap between theoretical concepts and practical implementations, providing you with the knowledge and tools to revolutionize industries and drive innovation. Delve into real-world applications and cutting-edge research that showcase how deep learning is redefining engineering processes, optimizing energy systems, and reshaping financial markets. This book: Explores deep learning applications across engineering, energy, and finance, highlighting diverse use cases and industry-specific challenges. Discovers how deep learning is driving breakthroughs in predictive maintenance, energy optimization, algorithmic trading, and risk management. Illustrates all the concepts connected to Deep Learning from head and heart with real-time practical examples and case studies. Stresses on skills needed to tackle future challenges, with a focus on emerging deep learning technologies oriented towards Solar Energy, SOM’s, Stock Market, Speech Technology and Many more. Whether you're a student eager to explore the latest advancements or a seasoned R&D professional seeking to enhance your skill set, this book offers invaluable insights and practical guidance to elevate your expertise.
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 in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.