Artificial Intelligence and Deep Learning

Artificial Intelligence and Deep Learning offers a comprehensive guide to understanding and applying the core principles of intelligent systems. It explores the evolution of AI, the fundamentals of machine learning, and the powerful techniques of deep learning that enable machines to learn from vast amounts of data.
Structured for both beginners and advanced learners, the book covers key AI concepts, including search algorithms, logic and reasoning, expert systems, and intelligent agents. It then transitions to machine learning foundations such as supervised, unsupervised, and reinforcement learning. The deep learning section provides in-depth coverage of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and advanced architectures like transformers.
Each chapter blends theoretical understanding with practical examples, programming exercises (primarily in Python), and real-world case studies in areas like healthcare, finance, autonomous systems, and natural language processing.
Designed for students, educators, and professionals across computer science, engineering, data science, and related fields, this book serves as both a learning resource and a reference. With its clear structure and up-to-date content, Artificial Intelligence and Deep Learning equips readers with the tools and knowledge to navigate and contribute to the rapidly advancing AI landscape.