This collection of deep learning and Large Language Model (LLM) lectures by Andrej Karpathy provides one of the most practical and beginner-friendly paths for understanding modern artificial intelligence systems from first principles.
The series begins with an introduction to Large Language Models, explaining how models like GPT work, how they are trained, and why transformer architectures became the foundation of modern generative AI systems.
Learners then dive into neural networks and backpropagation through the famous micrograd project, where neural network components are implemented from scratch in Python. This section helps students understand gradients, automatic differentiation, and optimization at a fundamental level.
The “makemore” series introduces language modeling concepts using practical coding examples. Learners build progressively more advanced neural network architectures for text generation and understand how AI models learn patterns from language datasets.
Additional lectures explore multilayer perceptrons (MLPs), activation functions, gradient flow, and normalization techniques such as Batch Normalization. These topics are essential for understanding how deep neural networks train effectively.
