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

Yes, you can browse and deploy models for NLP, vision, and time series from the built-in template marketplace.

What neural network architectures are supported?

Flow Nexus Neural supports a wide variety of architectures including feedforward, LSTM, GAN, autoencoder, and transformer models.

What are the prerequisites for using Flow Nexus Neural?

You need to add the Flow Nexus MCP server to Claude and have a valid account via the Flow Nexus CLI to authenticate.

How does distributed training work in this skill?

The skill initializes clusters in E2B sandboxes, deploying parameter servers and worker nodes to train models across a distributed mesh, ring, or star topology.

Flow Nexus Neural Networks

Name: Flow Nexus Neural Networks
Author: zellycloud

byzellycloud

Ciencia de Datos y ML

Trains and deploys complex neural network architectures within distributed E2B sandbox environments for scalable machine learning workflows.

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Flow Nexus Neural provides a comprehensive framework for managing the entire machine learning lifecycle directly within Claude Code. It enables developers to design custom architectures—including transformers, LSTMs, and GANs—and train them across distributed clusters using E2B sandboxes. Whether you are utilizing pre-trained templates from the marketplace or orchestrating large-scale federated learning across multiple worker nodes, this skill streamlines the transition from model architecture to production-ready inference without leaving your development environment.

Características Principales

Advanced federated learning capabilities for decentralized data training
Integrated marketplace for deploying pre-trained model templates
Multi-architecture support including Transformer, LSTM, GAN, and Feedforward
0 GitHub stars
Distributed training clusters using isolated E2B sandboxes
Comprehensive performance benchmarking and validation workflows

Casos de Uso

Building and training custom sentiment analysis models using Transformer architectures
Deploying and benchmarking production-grade image classifiers from the marketplace
Orchestrating large-scale time-series forecasting with distributed LSTM clusters

Acerca de

Características Principales

Advanced federated learning capabilities for decentralized data training
Integrated marketplace for deploying pre-trained model templates
Multi-architecture support including Transformer, LSTM, GAN, and Feedforward
0 GitHub stars
Distributed training clusters using isolated E2B sandboxes
Comprehensive performance benchmarking and validation workflows

Casos de Uso

Building and training custom sentiment analysis models using Transformer architectures
Deploying and benchmarking production-grade image classifiers from the marketplace
Orchestrating large-scale time-series forecasting with distributed LSTM clusters