The text begins by discussing the core concepts of computational intelligence and the mathematical and theoretical underpinnings of generative models. It explores different artificial intelligence architectures, while also addressing the integration of hybrid approaches and multi-modal learning techniques. Features:Discusses advanced generative models, including Generative Adversarial Networks, Variational Autoencoders, diffusion models, and transformer-based architectures. Provides insights into emerging fields like quantum artificial intelligence, artificial intelligence-powered automation, and metaverse applications, ensuring that readers stay ahead in the fast-evolving artificial intelligence landscape. Presents practical case studies, showing how artificial intelligence models are applied in real-world scenarios, such as artificial intelligence-driven medical imaging, fraud detection, and intelligent automation. Showcases a practical and hands-on approach by providing step-by-step coding examples, Python-based implementations, and tutorials using frameworks. Covers artificial intelligence ethics, responsible artificial intelligence deployment, and global regulatory frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, mathematics, artificial intelligence, and machine learning.
