"From RNNs to Transformers: The Evolution of Attention in NLP"

Join us on a captivating journey through the evolution of attention mechanisms in natural language processing (NLP). Discover how Recurrent Neural Networks (RNNs) paved the way for advanced models like LSTMs and GRUs, tackling challenges of memory and long-range dependencies. Explore the groundbreaking introduction of attention mechanisms by Bahdanau and Luong, revolutionizing tasks like translation and summarization. Finally, witness the transformative power of the Transformer architecture, which fully embraces self-attention, reshaping the landscape of NLP forever.