Real-World Applications of Artificial Intelligence and Machine Learning in Power Systems: A Code ApproachКНИГИ » ПРОГРАММИНГ
Название: Real-World Applications of Artificial Intelligence and Machine Learning in Power Systems: A Code Approach Автор: T. Mariprasath, V. Kirubakaran Издательство: Nova Science Publishers Год: 2025 Страниц: 248 Язык: английский Формат: pdf (true) Размер: 20.5 MB
Dive into the cutting-edge world of Artificial Intelligence and Machine Learning with this comprehensive guide, designed for both novices and experts alike. This book meticulously explores the core principles, development, and practical applications of AI and ML technologies. Beginning with an introduction to AI, the book navigates through the evolution and principles of Artificial Intelligence, delving deep into various neural network architectures, including feedforward, convolutional, recurrent, and generative adversarial networks. Each chapter is enriched with practical examples such as rainfall prediction, image analysis, sales forecasting, and financial data analysis, making complex concepts accessible and relatable. This book also offers an extensive overview of essential Machine Learning libraries and tools, including NumPy, Pandas, TensorFlow, and PyTorch. Readers will gain insights into both supervised and unsupervised learning algorithms, from logistic regression and decision trees to k-means clustering and Gaussian mixture models.
In the latter part, the focus shifts to real-world applications of Machine Learning in power systems, renewable energy, electric vehicles, fuel cells, and hydrogen production. Topics such as fault detection in power grids, energy theft detection, solar and wind energy forecasting, and predictive maintenance for electric vehicles and fuel cells are comprehensively covered, demonstrating the transformative impact of ML in these sectors. Whether you are an aspiring data scientist, an academic, or a professional in the field, this book is your essential resource for mastering the intricacies of AI and ML and applying these technologies to solve real-world problems.
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that specifically concentrates on creating algorithms that allow computers to acquire knowledge and make forecasts by analysing data. Multiple libraries support the creation and execution of machine learning models. Scikit-learn is a popular Python library that provides tools for analysing and modelling data. It supports a wide range of supervised and unsupervised learning methods. It is especially well-liked for its use in Deep Learning tasks. Keras is a neural networks API that simplifies and accelerates the creation of Deep Learning models. It is compatible with TensorFlow, Theano, and CNTK. PyTorch, created by Facebook’s AI Research department, offers a versatile and user-friendly interface for constructing neural networks. It is renowned for its dynamic computation graph, which streamlines the process of working with intricate designs.
NLTK, short for Natural Language Toolkit, is a comprehensive Python toolkit specifically developed for manipulating and analysing human language data, such as text. The software offers a range of tools for several natural language processing (NLP) tasks, including tokenization, part-of-speech tagging, stemming, lemmatization, and parsing. NLTK is extensively utilised in both academic and industrial settings for the purpose of researching and developing applications in the field of natural language processing (NLP). NLTK offers several important features, such as text processing functions for tasks like tokenizing, stemming, and lemmatizing text.
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