This deep learning series provides a clear and intuitive explanation of how modern artificial intelligence systems work, starting from basic neural networks all the way to advanced transformer models and large language models (LLMs).

The course begins by answering a fundamental question: what is a neural network? It explains how machines learn patterns from data and how neural networks are structured to process information. Then it moves into gradient descent, showing how models improve by minimizing errors during training.

A major focus of the series is backpropagation, where learners understand step-by-step how neural networks adjust their internal parameters to learn effectively. Both conceptual and mathematical explanations are included to make the idea accessible.

The course then transitions into modern AI systems, introducing large language models and explaining how transformers work. It explores GPT architecture, attention mechanisms, and how AI systems store and retrieve factual knowledge.

Finally, it connects deep learning to real-world applications such as image and video generation, showing how complex AI systems are built from simple mathematical principles.

By the end of this series, learners gain a strong conceptual understanding of deep learning, from basic neural networks to state-of-the-art AI models like GPT and transformers used today.