Neural Networks: A Concise Theoretical Foundation provides a comprehensive and structured overview of Neural Networks, covering basics as well as advanced topics. The book begins with basic concepts such as perceptrons, gradient-based learning, and optimization, then progresses through convolutional and recurrent networks, attention mechanisms, graph neural networks, and transformer-based models. Further sections explore generative models, reinforcement learning, biological perspectives, and the development of large-scale intelligent systems. The material is intended for students, researchers, and professionals who aim for both theoretical depth and practical understanding of modern Neural Network methods.

This book was written to bring clarity and structure to the subject by presenting a unified framework that connects mathematical foundations with recent innovations. It aims to support learners and practitioners who want a single reference that explains both the fundamental ideas and the developments that have shaped current research and technology in artificial intelligence.