Page cover

Neural Networks

  • Flux.jl Integration: Seamless integration with Flux.jl for state-of-the-art neural network capabilities with optimized performance.

  • Multiple Architectures: Support for a wide range of network architectures including feed-forward, recurrent (LSTM, GRU, RNN), convolutional, and transformer networks.

  • Bidirectional Networks: Support for bidirectional recurrent networks for improved sequence modeling.

  • Model Management: Comprehensive model training, validation, saving, and loading capabilities with version control and early stopping.

  • Agent-Specific Networks: Specialized neural network management for individual agents with personalized configurations and model sharing.

  • Transfer Learning: Ability to use pre-trained models and fine-tune them for specific tasks with layer freezing.

  • Distributed Training: Support for distributed training across multiple machines for large models with data parallelism.

  • Hyperparameter Optimization: Automated hyperparameter tuning using swarm intelligence algorithms (DE, PSO, DEPSO).

  • Model Visualization: Tools for visualizing network architecture, training progress, and activation patterns with interactive dashboards.

  • Ensemble Methods: Support for ensemble learning with multiple neural networks for improved performance and robustness.

Last updated