This comprehensive learning series by Andrej Karpathy provides one of the most intuitive and practical introductions to Large Language Models (LLMs), neural networks, and GPT systems. It is designed for learners who want to deeply understand how modern AI is built from the ground up.
The course begins with foundational concepts such as neural networks and backpropagation, explained through simple and intuitive coding examples like micrograd. This helps learners understand how machines learn and optimize through gradients.
Next, the series moves into language modeling using makemore, where you learn how models predict sequences and generate text step by step. You will explore different architectures such as MLPs, activation functions, Batch Normalization, and how these components improve model performance.
The course also dives into building a tokenizer from scratch, understanding how text is broken into tokens for AI processing. After that, you will learn how GPT models are implemented from scratch and how to reproduce GPT-2 using real code.
Advanced topics include optimization techniques, training dynamics, and scaling models effectively. The series also covers intuition behind machine learning mathematics, linear regression, NumPy, and visualization tools to strengthen foundational knowledge.
By the end of this course, you will understand how GPT-style models work internally, how language models are trained, and how to build them step by step from scratch with strong practical and theoretical understanding.
