The Transformer architecture, introduced in the paper "Attention Is All You Need" (Vaswani et al., 2017), revolutionized natural language processing by replacing recurrent neural networks with self-attention mechanisms. Below are key papers and resources related to Transformers:
Key Papers
Attention Is All You Need
📘 Original paper introducing the Transformer model.BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
🧠 A groundbreaking model for bidirectional attention.GPT-3: Language Models are Few-Shot Learners
💬 Showcasing the power of large-scale language models.
Resources
- 📚 Deep Learning Book - Transformer Chapter
- 🧪 Transformer Implementation Tutorials
- 🌐 Research Papers on Attention Mechanisms
Summary
The Transformer model's innovation lies in its self-attention mechanism, enabling parallel processing and better handling of long-range dependencies. Its influence spans from NLP to computer vision, making it a cornerstone of modern AI research. 🔧
For further exploration, check out our Transformer Tutorials or related papers. 🚀