Ethical Considerations in Natural Language Processing

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Page 3 of 4

Delves into the business impact of AI, ethical challenges, and consumer trust in technology.

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

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Covers the basics of Natural Language Processing, including challenges, linguistic processing levels, and the impact of power laws.

Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Explores the efficiency of training large language models and the environmental impact of AI.

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

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

Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.