Deep Learning for NLP

This lecture covers the fundamentals of deep learning for natural language processing (NLP), focusing on word vector composition, embeddings, context representations, and learning techniques like Word2vec and Glove. It delves into continuous bag of words (CBOW) and skip-gram models, explaining the softmax function, and the challenges of vanishing gradients in recurrent neural networks. The lecture also introduces the transformer model, self-attention mechanism, and the integration of structured and unstructured knowledge in NLP tasks. It concludes with discussions on the successes, challenges, and ethical considerations in NLP.