Introduction to Natural Language Processing

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Page 1 of 4

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

Coreference Resolution

Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.

Multilingual NLP: Challenges and Innovations

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

Natural Language Processing: Understanding Transformers and Tokenization

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.

Word Embeddings: Introduction and Applications

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Model Analysis

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

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Coreference Resolution

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

Introduction to Modern Natural Language Processing

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

Pretraining Sequence-to-Sequence Models: BART and T5

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