Название: Neural Network Advancements in the Age of AI
Автор: Sarah Benziane, Fatiha Guerroudji Meddah
Издательство: IGI Global
Год: 2025
Страниц: 576
Язык: английский
Формат: pdf (true), epub
Размер: 28.4 MB

Emerging trends such as Explainable Artificial Intelligence (XAI), few-shot learning, and neural architecture search (NAS) push the boundaries of current neural networks. These cutting-edge networks are transforming the design and efficiency in modern applications, including computer vision, natural language processing (NLP), and autonomous systems. They enable engineers and data scientists to design efficient models, accelerating innovation in sectors like healthcare, biotechnology, and smart systems. The practical implementation techniques of these advanced neural networks are ideal for optimizing neural networks in real-world scenarios and solving real-world problems. Neural Network Advancements in the Age of AI provides a comprehensive exploration of neural networks, emphasizing both the theoretical foundations and practical applications across various fields. It bridges the gap between theory and practice, making neural networks accessible to both researchers and practitioners. Covering topics such as machine translation, predictive maintenance, and skill acquisition, this book is an excellent resource for AI engineers, data scientists, software developers, healthcare professionals, financial practitioners, engineers, computer scientists, professionals, researchers, scholars, academicians, and more.

Deep Learning has emerged as a transformative branch of Artificial Intelligence (AI), revolutionizing diverse domains with its ability to process and analyze large, complex datasets. The Chapter 2 provides an in-​depth exploration of Deep Learning techniques, emphasizing their foundational concepts, architecture, and applications. It begins with the discussion of the history of Deep Learning and the transition from Machine Learning to Deep Learning. An introduction to neural networks, including convolutional and recurrent architecture, and highlights their unique capacity for hierarchical feature extraction. The discussion extends to advanced techniques such as Generative AI, Large Language Model and Transformer Model, showcasing their adaptability across tasks. Focusing on practical implications, the chapter examines the impact of Deep Learning in fields like healthcare, finance, autonomous systems, and natural language processing. By the end of the chapter, readers will gain a comprehensive understanding of Deep Learning capabilities, limitations, and transformative potential.