دانلود کتاب کاربردهای هوش مصنوعی و یادگیری ماشین در سیستم‌های قدرت در دنیای واقعی – رویکردی مبتنی بر کد

This book provides a thorough overview of the exciting fields of machine learning and artificial intelligence, with a focus on practical applications and creative implementations in a range of sectors. This book attempts to give readers the fundamental ideas, technical specifics, and application-based insights required to navigate the rapidly evolving fields of artificial intelligence (AI) and machine learning (ML), which have profoundly changed a number of industries, from energy management to electric automobiles. The book begins by examining the foundations of artificial intelligence, which encompass the evolution, tenets, and varieties of neural networks, such as feedforward, convolutional, and recurrent networks. We support each of these networks with practical case studies, ranging from image processing to rainfall prediction and sales forecasting. We describe the complex architecture of networks like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), providing both professionals and learners with real-world examples through applications in EGC data analysis and missing data imputation. Subsequently, readers will discover a comprehensive summary of the fundamentals of machine learning, encompassing both supervised and unsupervised techniques. The book discusses the necessity for specialised libraries that facilitate effective data processing and model creation through studies of well-known tools like TensorFlow, PyTorch, and Scikit-learn, as well as lesser-known yet potent libraries like FastText and Dlib. The third section focuses on machine learning algorithms, categorising them into supervised, unsupervised, and semi-supervised learning techniques. We describe each algorithm in detail, providing real-world examples and applications to help readers understand the advantages and disadvantages of various approaches. Lastly, the book examines particular machine learning applications in a range of industries, including electric vehicles, renewable energy, and power systems. The information on problem detection, energy demand forecasting, predictive maintenance, and hydrogen production optimisation provides a glimpse of how machine learning (ML) might improve resilience and efficiency in these areas. This book aims to provide readers with a comprehensive yet practical resource, bridging the gap between AI/ML concepts and their revolutionary applications. This book offers the fundamental knowledge required to comprehend, apply, and innovate in the quickly developing fields of artificial intelligence and machine learning, regardless of your background—student, researcher, or industry expert.