Words Tokens: Lexical Level Overview

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Explores lexicons, n-grams, and language models, emphasizing their importance in recognizing words and the effectiveness of n-grams for various tasks.

Words and Tokens: Language Models and Probabilities

Reviews language models, tokenization, and probability estimation in NLP systems.

Neural Word Embeddings: Learning Representations for Natural Language

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

Language Models: Fixed-context and Recurrent Neural Networks

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

Words, tokens, n-grams and Language Models

Explores words, tokens, n-grams, and language models, focusing on probabilistic approaches for language identification and spelling error correction.

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.

Prompting and Alignment

Explores prompting, alignment, and the capabilities of large language models for natural language processing tasks.

Modern NLP and Ethics in NLP

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

Neural Word Embeddings

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

Classical Language Models: Foundations and Applications

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