Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.
Explores model interpretation, compilation via partial evaluation, function calls, and the transition to partial evaluation, emphasizing the importance of model interpreters in supporting modeling languages.
Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.
