Overview:

This reviews several research papers on Transformer models, originally introduced in 2017 as a breakthrough architecture in deep learning. Transformers replaced traditional recurrent models (RNNs/LSTMs) by using self-attention, enabling better performance on sequence, language, and many other tasks.

What’s Included?

• Summary of key Transformer research papers.
• Applications of Transformers across different fields (NLP, data science, cybersecurity, vision, etc.)
• Advantages and limitations of Transformer architecture.
• Insights on how Transformers have shaped modern AI.

Overview: This repository contains a review of research papers on Transformer models, a deep learning architecture introduced in 2017 that replaces recurrence with self-attention. We explore their applications, strengths, limitations, and impact on modern AI.