NLP 101 is a curated repository of resources for individuals interested in Deep Learning and Natural Language Processing. It aims to provide a structured learning path, covering foundational mathematics, core deep learning concepts, and advanced NLP techniques, targeting students, researchers, and practitioners in the field. The repository offers a consolidated list of lectures, books, blogs, and libraries, reducing the effort required to discover high-quality learning materials.

How It Works

This repository functions as a comprehensive, annotated bibliography. It categorizes resources by topic (Mathematics, Deep Learning, NLP, Libraries) and provides brief descriptions, source links, and difficulty assessments. The selection criteria prioritize unique coverage, varying difficulty levels, and practical application, ensuring a well-rounded learning experience.

Quick Start & Requirements

This repository is a collection of links and does not require installation. Users will need internet access to view the linked resources.

Highlighted Details

• Extensive coverage of foundational mathematics (Statistics, Linear Algebra, Calculus) with intuitive explanations.
• Curated list of key deep learning resources, including theoretical texts and practical code implementations.
• Detailed sections on NLP, featuring seminal books, university courses (e.g., Stanford's CS224N), and code-first approaches.
• Comprehensive listing of essential NLP libraries like Hugging Face Transformers, spaCy, and PyTorch, with links to tutorials.

Maintenance & Community

The repository is maintained by Huffon, with an open invitation for recommendations and suggestions. It does not currently list specific community channels or a formal roadmap.

Licensing & Compatibility

The repository itself is a collection of links and does not have a specific license. The linked resources are subject to their own respective licenses.

Limitations & Caveats

The repository is a curated list and does not provide direct access to the learning materials themselves, requiring users to navigate to external sites. The content is primarily focused on academic and theoretical resources, with less emphasis on deployment or production-level considerations.