Introduction to Modern Natural Language Processing

Related lectures (32)

Page 2 of 4

Covers the development of transformers and their impact on attention mechanisms in NLP.

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

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

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Covers the importance of multilingual NLP and the challenges in scaling language models.

Introduces financial markets, time series, machine learning applications in finance, and natural language processing.

Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Covers neural word embeddings and methods for learning word representations in natural language processing.

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.