
Deep Learning in Engineering, Energy and Finance
Principals and Applications
- 448 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Deep Learning in Engineering, Energy and Finance
Principals and Applications
About this book
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.
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Editors
- List of contributors
- 1 Convolutional neural networks: Concepts and applications in solar energy, engineering, and finance
- 2 From neurons to networks: Unravelling the secrets of artificial neural networks and perceptrons
- 3 Navigating deep learning models and health monitoring infrastructure financing in smart cities: Review from legal perceptions and future innovations
- 4 A comprehensive review of learning rules and architecture of perceptron in artificial neural networks (ANNs)
- 5 Deep convolution models: Basic definitions, types, and applications in solar energy, engineering, and finance
- 6 Self-organizing maps: Concept, architecture, and use cases in engineering and finance
- 7 Applications of machine learning/deep learning for power grid: Reliability and resilience of smart grid systems
- 8 Energy forecasting of electric vehicles at charging stations using machine learning
- 9 Nifty index: Integrating deep learning models for future predictions and investments
- 10 Convolutional neural network for developing robust speech technology
- Index