This repository serves as a curated collection of resources for in-depth study of the Transformer architecture, targeting researchers, engineers, and students interested in Natural Language Processing. It provides a structured learning path through papers, blog posts, lectures, code walkthroughs, and follow-up research, aiming to demystify the Transformer's mechanics and its evolution.

How It Works

The project organizes learning materials into distinct categories: original paper reviews, explanatory blog posts, academic lectures, practical code implementations, and subsequent influential papers. This multi-faceted approach caters to different learning styles, from theoretical understanding via paper discussions and lectures to practical application through code walkthroughs and exploring follow-up research like BERT and GPT.

Quick Start & Requirements

This repository is a collection of links and does not require installation or execution. All linked resources are external.

Highlighted Details

• Comprehensive coverage of the original "Attention Is All You Need" paper through multiple review formats.
• Links to highly regarded blog posts like "The Illustrated Transformer" for intuitive explanations.
• Inclusion of code walkthroughs in PyTorch and TensorFlow, including annotated versions of the paper.
• Extensive list of follow-up papers and resources on models like BERT, GPT, XLNet, and Reformer.

Maintenance & Community

This is a static collection of curated links. There is no active development or community interaction associated with this repository itself.

Licensing & Compatibility

The repository itself contains no code and is a collection of links to external resources. The licensing of the linked content varies by source.

Limitations & Caveats

As a curated list of external resources, the repository's content is dependent on the availability and maintenance of the linked external sites. Some links may become outdated or inaccessible over time.