Data-Driven Insights: NLP and AI Applications

Related lectures (31)

Page 3 of 4

Introduces the course on Modern Natural Language Processing, covering its significance, applications, challenges, and advancements in technology.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

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

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

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

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

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

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

Covers EPFL's Computer Science School, faculty expertise, EDIC program requirements, and post-PhD career paths.

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