karpathy/LLM101nArchived
LLM101n
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.
Features
- Neural Computation Frameworks - Utilizes dynamic tensor libraries to define and train neural network architectures through automatic differentiation.
- Machine Learning Curricula - Provides a comprehensive instructional program guiding students through the fundamental principles of large language models.
- Neural Network Implementations - Implements core neural network architectures and training pipelines from scratch to demonstrate fundamental mechanics.
- Open Source Learning Resources - Facilitates self-paced study and collaborative exploration of complex technical subjects through accessible materials.
- Interactive Notebooks - Delivers educational content through interactive documents that allow real-time code execution and model visualization.
- Technical Manuals - Demonstrates the step-by-step construction and training of deep learning architectures from scratch.
- Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
- Courses and Tutorials - Course on building AI applications.
- Learning Resources - Course on building a storyteller model.
- Personal Projects - Educational repository for building and understanding large language models.
- Curriculum Development Tools - Guides students through the end-to-end process of building and training modern artificial intelligence models.
- Prototyping Environments - Provides a lightweight environment for experimenting with model design and training configurations.
- Memory Mapping Utilities - Maps large binary datasets directly into memory to minimize overhead during high-frequency training iterations.
- Technical Skill Training - Bridges the gap between theory and implementation through hands-on deep learning exercises.
