Deep Learning Foundations: From Perception to Large Language Models

Deep learning has evolved rapidly, moving from basic image perception to the complex natural language understanding that powers modern artificial intelligence. To work with today's AI, you need to understand the fundamental architectures that make these breakthroughs possible. This text-based course guides you through the foundational concepts of deep learning, tracing the path from neural networks and perception models to advanced transformer architectures like BERT and GPT. \n\nWhat you'll learn:\n- Understand the core principles of neural networks and how machines process visual and sequential data.\n- Explore the mechanics of attention mechanisms and the breakthrough of transformer architectures.\n- Analyze the structural differences and use cases for encoder-based models like BERT and decoder-based models like GPT.\n- Learn the basics of prompt engineering and how modern language models are adapted for practical applications.\n- Discover how vector databases and retrieval-augmented generation (RAG) patterns support language model deployment.\n\nYou will start with essential deep learning terminology and foundational perception concepts before progressing to attention mechanisms, transformer blocks, and modern large language model implementations. This course is designed for beginners who want to build a strong, conceptual understanding of modern AI without needing prior advanced programming experience. Start your journey into the world of deep learning and large language models today.