This Stanford CS336 course, Language Modeling from Scratch, is an advanced deep learning program that teaches how modern large language models are built from the ground up. It focuses on both the theoretical foundations and the engineering challenges involved in training large-scale AI systems.
You will begin with an introduction to language modeling and tokenization, learning how raw text is converted into tokens that can be processed by neural networks. This step is essential for all modern NLP and LLM systems.
Next, the course introduces PyTorch fundamentals and resource accounting, explaining how to manage computation, memory, and efficiency when training large models. These concepts are critical for scaling AI systems in real-world environments.
You will then explore model architectures and hyperparameter tuning, learning how design choices impact performance, stability, and generalization in language models.
A major highlight of the course is Mixture of Experts (MoE), an advanced architecture that improves scalability and efficiency by activating only parts of a model during computation.
Finally, the course covers GPU utilization and distributed training, showing how large models are trained efficiently using modern hardware systems.
By the end of this course, you will understand how to design, implement, and scale language models from scratch.
This course is ideal for AI researchers, engineers, and advanced machine learning students.
