Introduction to Transformers in Deep Learning

Deep learning has been completely transformed by the introduction of the Transformer architecture, which powers the most advanced AI systems we use today. But how do these models actually process language, retain context, and generate coherent text? This text-based course guides you through the core concepts of Transformers, taking you from foundational neural network limitations to the self-attention mechanism and modern generative AI applications. What you'll learn: - Understand the core limitations of traditional recurrent networks and how Transformers solved them - Master the mechanics of self-attention, query-key-value mapping, and multi-head attention - Explore the encoder-decoder architecture and how it processes sequential data in parallel - Discover how modern large language models leverage Transformer blocks for text generation - Learn the foundations of prompt engineering and how retrieval-augmented generation (RAG) connects to Transformer outputs - Practice analyzing Transformer workflows through clear, step-by-step written explanations and conceptual walkthroughs Starting with essential terminology and foundational definitions, you will progress step-by-step from basic attention mechanisms to the structural design of state-of-the-art language models. This course is designed for beginners, developers, and technology enthusiasts who want to understand the mechanics of modern AI without needing prior advanced deep learning experience. Start reading today to unlock the inner workings of the technology shaping the future of artificial intelligence.