Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.
Introduces Natural Language Processing, covering text preprocessing, sentiment analysis, and topic analysis, with a focus on building a climate change risk index.
Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.
Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.
Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.
Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.
