kjw0612/awesome-rnn

Why this repo matters

• •Curated archive of foundational RNN resources remains relevant for understanding sequential modeling history and legacy system maintenance.
• •High star count indicates enduring value as a reference for LSTM and GRU architectures despite transformer dominance.
• •Comprehensive categorization covers theory, code, and applications useful for researchers studying pre-transformer deep learning techniques.