Language model

Related lectures (32)

Page 2 of 4

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.

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

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

Addresses data collection, annotation processes, and biases in natural language processing.

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.

Introduces neural word embeddings and dense vector representations for natural language processing.