Neural Language Models

Neural language models are a class of machine learning models that use neural networks to predict the probability of sequences of words or tokens in natural language. They learn distributed representations of language from large text corpora, enabling tasks like text generation, translation, and classification. These models form the foundation of modern natural language processing (NLP) systems, including large language models (LLMs) like GPT and BERT.

Also known as: Neural LMs, Neural NLP Models, Language Models, LLMs, Neural Network Language Models

Why learn Neural Language Models?

Developers should learn neural language models when building applications involving natural language understanding, generation, or analysis, such as chatbots, content summarization, or sentiment analysis. They are essential for leveraging state-of-the-art NLP capabilities, as they outperform traditional statistical methods by capturing complex linguistic patterns and context. This knowledge is crucial for roles in AI, data science, or software development focusing on text-based features.

Learning Resources

• tutorial: The Illustrated Transformer
• course: Stanford CS224N: Natural Language Processing with Deep Learning
• docs: Hugging Face Transformers Documentation
• book: Neural Network Methods for Natural Language Processing by Yoav Goldberg
• docs: Attention Is All You Need - Original Paper