LLM101n is an educational machine learning curriculum and open-source resource designed to teach the fundamental principles and practical implementation of large language models. It functions as a technical manual that guides users through the end-to-end process of building and training neural network architectures from scratch using a dynamic tensor library for automatic differentiation and GPU-accelerated computation.
The project distinguishes itself through interactive, notebook-based instruction that allows for real-time visualization of training processes. It supports rapid experimentation and model prototyping by utilizing memory-mapped data loading to handle large datasets efficiently and a modular architecture that separates data processing, model definition, and optimization loops.
The repository covers a broad range of machine learning engineering tasks, providing a structured environment for technical skill acquisition. It serves as a centralized archive for educational content and project documentation, maintaining all materials as version-controlled assets to ensure long-term accessibility and integration with standard development workflows.
