Courses that teach you how to customize and fine-tune foundation models for specific use-cases. Learn about techniques like few-shot learning and fine-tuning, and how to create domain-specific language models. Understand how to reduce the cost and improve performance of trained models, utilize GPUs effectively, and create installers for faster deployment. Explore topics like dataset preparation, model dissection, and automated framework replication across different datasets. Master the architecture, pre-training, prompt engineering, and inference performance of language models.