Flow Nexus Neural Networks Deploy, train, and manage neural networks in distributed E2B sandbox environments. Train custom models with multiple architectures (feedforward, LSTM, GAN, transformer) or use pre built templates from the marketplace. Prerequisites Core Capabilities 1. Single Node Neural Training Train neural networks with custom architectures and configurations. Available Architectures: feedforward Standard fully connected networks lstm Long Short Term Memory for sequences gan Generative Adversarial Networks autoencoder Dimensionality reduction transformer Attention based models Training Tiers: nano Minimal resources (fast, limited) mini Small models small Standard models medium Complex models large Large scale training Example: Train Custom Classifier Example: LSTM for Time Series Example: Transformer Architecture 2. Model Inference Run predictions on trained models. Response: 3. Template Marketplace Browse and deploy pre trained models from the marketplace. List Available Templates Response: Deploy Template 4. Distributed Training Clusters Train large models across multiple E2B sandboxes with distributed computing. Initialize Cluster Response: Deploy Worker Nodes Connect Cluster Topology Start Distributed Training Federated Learning Example: Monitor Cluster Status Response: Run Distributed Inference Terminate Cluster 5. Model Management List Your Models Response: Check Training Status Response: Performance Benchmarking Response: Create Validation Workflow 6. Publishing and Marketplace Publish Model as Template Rate a Template Common Use Cases Image Classification with CNN NLP Sentiment Analysis Time Series Forecasting Federated Learning for Privacy Architecture Patterns Feedforward Networks Best for: Classification, regression, simple pattern recognition LSTM Networks Best for: Time series, sequences, forecasting Transformers Best for: NLP, attention mechanisms, large scale text GANs Best for: Generative tasks, image synthesis Autoencoders Best for: Dimensionality reduction, anomaly detection Best Practices 1. Start Small : Begin with nano or mini tiers for experimentation 2. Use Templates : Leverage marketplace templates for common tasks 3. Monitor Training : Check status regularly to catch issues early 4. Benchmark Models : Always benchmark before production deployment 5. Distributed Training : Use clusters for large models ( 1B parameters) 6. Federated Learning : Use for privacy sensitive data 7. Version Models : Publish successful models as templates for reuse 8. Validate Thoroughly : Use validation workflows before deployment Troubleshooting Training Stalled Low Accuracy Increase epochs Adjust learning rate Add regularization (dropout) Try different optimizer Use data augmentation Out of Memory Reduce batch size Use smaller model tier Enable gradient accumulation Use distributed training Related Skills flow nexus sandbox E2B sandbox management flow nexus swarm AI swarm orchestration flow nexus workflow Workflow automation Resources Flow Nexus Docs: https://flow nexus.ruv.io/docs Neural Network Guide: https://flow nexus.ruv.io/docs/neural Template Marketplace: https://flow nexus.ruv.io/templates API Reference: https://flow nexus.ruv.io/api Note : Distributed training requires authentication. Register at https://flow nexus.ruv.io or use npx flow nexus@latest register .

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