Large language model

Related lectures (30)

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Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Delves into the use of custom GPTs for academic writing, highlighting the balance between AI assistance and traditional learning methods.

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Covers the second assignment for the CS-552: Modern NLP course, focusing on transfer learning and data augmentation.

Explores the evolution of modern NLP from GPT-2 to GPT-3, emphasizing in-context learning and the development of ChatGPT.

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.