This repository serves as a comprehensive collection of interview preparation notes for Natural Language Processing (NLP) algorithm engineers. It covers a wide range of topics, from fundamental concepts like Markov models and Conditional Random Fields to advanced deep learning architectures such as BERT, Transformers, and various fine-tuning techniques. The material is structured to help candidates systematically review and prepare for technical interviews in the NLP domain.

How It Works

The repository is organized thematically, with each section dedicated to a specific NLP area or algorithm. It presents concepts through a question-and-answer format, often detailing the motivation behind a technique, its core principles, common variations, advantages, disadvantages, and practical considerations. This structured approach facilitates learning and recall of key information relevant to NLP interviews.

Quick Start & Requirements

This repository is a collection of notes and does not require installation or execution. It is intended for reading and study.

Highlighted Details

• Extensive coverage of core NLP algorithms: HMM, MEMM, CRF, Word2Vec, FastText, ELMo, BERT, and Transformer variants.
• Detailed explanations of various NLP tasks: Named Entity Recognition (NER), Relation Extraction, Event Extraction, Text Classification, Text Matching, Question Answering, Dialogue Systems, Text Summarization, and Text Correction.
• In-depth discussion of modern NLP techniques including prompt tuning, LoRA, PEFT, and instruction tuning.
• Includes foundational machine learning concepts, optimization algorithms, and regularization techniques relevant to NLP.

Maintenance & Community

The repository is maintained by km1994. Further community interaction details are not specified in the README.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README.

Limitations & Caveats

The repository is a static collection of notes and does not provide code implementations or interactive demos. The depth of coverage for each topic may vary, and it is primarily focused on interview preparation rather than practical implementation guidance.