- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
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.
Contents
1. Convolutional Neural Networks: Concepts and Applications in Solar Energy, Engineering and Finance. 2. From Neurons to Networks: Unravelling the Secrets of Artificial Intelligence Neural Networks and Perceptron's. 3. Navigating Deep Learning Models and Health Monitoring Infrastructure in Smart Cities: Review from Legal Perspectives 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 Networks for Developing Robust Speech Technology.
