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June 25, 2025, 1:51 p.m.
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(Last updated: June 25, 2025, 1:51 p.m.)
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SOURCE: Benziane S. Neural Network Advancements in the Age of AI 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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. Preface Section 1. Mathematical and Theoretical Foundations Introduction to Neural Networks Deep Learning: Foundations, Architectures, and Transformative Applications Regularization of Generative Models The Revolution of Generative AI Section 2. Generative and Adversarial Models Exploring the Applications and Impact of Generative Adversarial Networks in Modern Technology Section 3. Specialized Architectures CNN in Medical Imaging: Advantages, Challenges, and Future Scope Leveraging Echo State Networks for DDoS Detection in Software- Defined Networking Enhancing Content Recommendation for Programming Education Using BERT- Based Techniques Section 4 Language, Translation, and Accessibility Evaluating Neural Network- Based Arabic Machine Translation: Challenges, Metrics, and Solutions Conversational Algerian Sign Language Recognition Using the Deep Learning Approach Section 5 Human Activities, Learning, and Behavior Decoding Human Activities: Algorithms, Frameworks, and Challenges in Recognition Systems vestigating Motivation for Skill Acquisition Among Indian Students Pursuing Professional Courses: A Predictive Analytics Framework With Artificial Neural Networks Data Quality Management Technique in Big Data Analytics for Machine Learning and Deep Learning Applications in Gaming Compilation of References
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