Which neural network architectures are supported?

Flow Nexus Neural supports feedforward, LSTM, GAN, autoencoder, and Transformer architectures for various AI tasks.

Can I use pre-trained models instead of training from scratch?

Yes, the skill features a Template Marketplace where you can browse, list, and deploy pre-trained models for common categories like NLP and Vision.

Does it support privacy-preserving machine learning?

Yes, it supports federated learning configurations where data remains on local nodes while only model updates are synchronized.

What infrastructure does this skill require?

It requires the Flow Nexus MCP server and utilizes E2B sandboxes to provide the compute environments for training and inference.

How does distributed training work in this skill?

You can initialize clusters consisting of parameter servers, worker nodes, and aggregators that collaborate to train large models across multiple sandboxes.

Flow Nexus Neural

Name: Flow Nexus Neural
Author: Activer007

byActiver007

数据科学与机器学习

Deploys and trains neural networks in distributed E2B sandbox environments for advanced machine learning workflows.

关于

Flow Nexus Neural provides a comprehensive framework for managing the entire machine learning lifecycle within Claude Code. It enables users to architect custom neural networks—ranging from simple feedforward models to complex Transformers—and train them either on single nodes or across distributed clusters using E2B sandboxes. With built-in support for federated learning, a template marketplace for pre-trained models, and integrated performance benchmarking, this skill bridges the gap between high-level AI interaction and robust, low-level model engineering and deployment.

主要功能

0 GitHub stars
Distributed training clusters utilizing E2B sandboxes with WASM optimization.
Integrated model marketplace to browse, deploy, and publish pre-trained templates.
Federated learning capabilities for secure training on distributed datasets.
Comprehensive performance benchmarking for latency, throughput, and accuracy.
Multi-architecture support including Transformers, LSTMs, GANs, and Autoencoders.

使用场景

Setting up distributed clusters for large-scale image classification or NLP tasks across multiple sandbox nodes.
Deploying and fine-tuning pre-trained sentiment analysis or vision models from the marketplace to speed up development.
Training custom time-series forecasting models using LSTM architectures for financial or IoT data.

关于

主要功能

0 GitHub stars
Distributed training clusters utilizing E2B sandboxes with WASM optimization.
Integrated model marketplace to browse, deploy, and publish pre-trained templates.
Federated learning capabilities for secure training on distributed datasets.
Comprehensive performance benchmarking for latency, throughput, and accuracy.
Multi-architecture support including Transformers, LSTMs, GANs, and Autoencoders.

使用场景

Setting up distributed clusters for large-scale image classification or NLP tasks across multiple sandbox nodes.
Deploying and fine-tuning pre-trained sentiment analysis or vision models from the marketplace to speed up development.
Training custom time-series forecasting models using LSTM architectures for financial or IoT data.