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 1 month ago

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 1 month ago