Advancing Beyond GPT-3: Architectures and Algorithms for Cutting-Edge Language Modeling

This advanced course focuses on the development of language models that exceed the capabilities of GPT-3. Students will learn about the latest techniques for building large-scale language models and explore cutting-edge research in the field of natural language processing.

The course covers advanced topics such as neural architecture search, transfer learning, and unsupervised learning. Students will gain a deep understanding of the mathematical and computational principles behind state-of-the-art language models, and how to apply these principles to build their own models.

Throughout the course, students will work on hands-on projects that involve building and training language models using popular deep learning frameworks such as PyTorch and TensorFlow. These projects will cover topics such as attention mechanisms, transformer architectures, and language modeling objectives.

By the end of the course, students will have a solid understanding of the latest architectures and algorithms for building large-scale language models, and be able to apply this knowledge to develop their own advanced models that surpass GPT-3.