Deep Learning for NLP

This lecture covers the fundamentals of Deep Learning for Natural Language Processing (NLP), including Neural Word Embeddings, Recurrent Neural Networks for Sequence Modeling, and Attentive Neural Modeling with Transformers. The instructor discusses the challenges of fixed context windows in early neural language models, the limitations of recurrent models in learning long-range dependencies, and the advancements brought by self-attention mechanisms in transformers. The presentation includes a detailed explanation of self-attention, multi-headed attention, and the architecture of a full transformer model with encoder and decoder blocks.