The textbook uses a unique "brick-by-brick" methodology to build understanding incrementally, starting from attention mechanisms and progressing systematically through the complete Transformer architecture. Each concept is explained with mathematical precision, intuitive visualizations, practical code examples, and chapter-end learning objectives that help readers consolidate key concepts. Every chapter also includes fully worked exercises and detailed solutions, enabling self-assessment and reinforcing both theoretical understanding and practical skills. Perfect for master’s students and professionals entering deep learning, this book bridges the gap between superficial tutorials and dense research papers. Through a series of hands-on Python labs, readers do not simply learn about Transformers, they progressively build the key components of the architecture, train and evaluate their models, and gain the practical skills needed to work confidently with modern Transformer-based systems.