Smart Applications of Artificial Intelligence and Big DataКНИГИ » ПРОГРАММИНГ
Название: Smart Applications of Artificial Intelligence and Big Data Автор: Salwa Belaqziz, Salma El Hajjami, Hicham Amellal, Redouan Lahmyed, Lahcen Koutti, Boulouz Abdellah, Inam Ullah Khan Издательство: CRC Press Год: 2025 Страниц: 344 Язык: английский Формат: epub (true) Размер: 11.3 MB
Smart Applications of Artificial Intelligence and Big Data covers a wide range of topics related to AI and Big Data, including Machine Learning, Deep Learning, natural language processing (NLP), computer vision, data analytics, and data mining. It focuses on the integration of these technologies to create smart applications, such as intelligent transportation systems, smart healthcare, smart cities, and smart grids.
This book comprises 21 chapters, each providing technical details pertaining to research, practical examples, and case studies to help readers understand the real-world applications of AI and big data technologies. The book also highlights cutting-edge research on AI and Big Data, including novel algorithms, tools, and techniques. It discusses the challenges and opportunities of using AI and Big Data to develop smart applications and provides recommendations for the development of responsible and transparent AI-based systems. This book is a valuable resource for researchers and professionals looking to stay up-to-date with the latest advancements in AI and Big Data and how they can be applied to solve real-world challenges.
Machine Learning and Deep Learning have spearheaded transformative changes across numerous industries, spanning healthcare, finance, autonomous vehicles, and natural language processing. Fueled by the analysis of extensive datasets, these algorithms demand substantial computational power for both training and inference. Graphics processing units (GPUs) have emerged as a pivotal technology, facilitating the acceleration of these computations and enabling the training of intricate models within reasonable timeframes. However, realizing the full potential of GPUs necessitates a meticulously configured and optimized environment, a task that can be formidable.
This chapter delves into the utilization of Docker containers as a solution to the challenges associated with creating and managing Machine Learning environments with GPU support. Docker containers offer a lightweight and portable approach to package applications alongside their dependencies, libraries, and system configurations. By encapsulating the entire environment within a container, researchers and practitioners can ensure consistent execution across diverse systems, eliminating the compatibility hurdles that often arise when deploying Machine Learning models on various hardware setups. The objective of this chapter is to showcase the advantages and effectiveness of Docker containers in addressing the complexities of GPU-enabled Machine Learning environments.
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