This repository provides comprehensive lecture and seminar materials for a Natural Language Processing (NLP) course, specifically the 2024 iteration. It's designed for students and practitioners seeking to understand and implement modern NLP techniques, from foundational concepts like word embeddings to advanced topics such as Large Language Models (LLMs) and Reinforcement Learning from Human Feedback (RLHF).

How It Works

The course material is structured weekly, covering key NLP areas. Each week includes lectures detailing theoretical concepts and practical approaches, seminars offering hands-on experience, and homework assignments to reinforce learning. The curriculum progresses from classical methods (e.g., Naive Bayes, SVMs) to neural network architectures (CNNs, RNNs, Transformers) and culminates in state-of-the-art LLM techniques, including prompting, efficient fine-tuning, and RLHF.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and installing Python dependencies via pip. Specific instructions for library installation and troubleshooting are available in a linked thread.
• Prerequisites: Python 3.x, standard ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers), and potentially GPU access for advanced assignments.
• Resources: Setup time varies based on familiarity with Python and ML environments. Running advanced models may require significant computational resources.
• Links: Course Syllabus (within README)

Highlighted Details

• Covers a broad spectrum of NLP topics, from traditional methods to cutting-edge LLM research.
• Includes practical assignments on word embeddings, text classification, language modeling, machine translation, and LLM fine-tuning.
• Features guest lectures and extra modules on domain adaptation and efficient inference.
• Explores analysis and interpretability techniques for various NLP models.

Maintenance & Community

The course materials are developed and maintained by Yandex Data School, with significant contributions from Elena Voita (course author), Mikhail Diskin, Ignat Romanov, Ruslan Svirschevski, and over 30 volunteers. Teaching Assistants (TAs) also play a crucial role.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README snippet. Users should verify licensing for commercial use or integration into closed-source projects.

Limitations & Caveats

The course materials are focused on the 2024 version; older materials may be found on different branches. While comprehensive, the practical implementation of some advanced topics (e.g., training large models) may require substantial hardware resources beyond typical development setups.