This course provides a clear and intuitive journey through deep learning, starting from the fundamentals of neural networks to the advanced architecture behind Large Language Models (LLMs) and modern AI systems.

You will first understand what a neural network is and how it learns using gradient descent. The course explains backpropagation both intuitively and mathematically, giving you a strong conceptual foundation of how models update their weights and minimize error.

As the course progresses, it explores transformers—the revolutionary architecture powering today’s most advanced AI systems. You will learn how attention mechanisms work step-by-step, how transformers process sequences, and how large language models store and retrieve information. The course also gives insight into how generative AI systems create images and videos using deep learning techniques.

By the end of this course, learners will understand the core mathematics behind neural networks, the mechanics of backpropagation, and the structure of transformer-based models that power modern AI tools and large-scale language systems.