How does distributed training work in this skill?

It initializes clusters across multiple E2B sandboxes, deploying parameter servers, worker nodes, and aggregators to handle large-scale datasets and complex computations.

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

Yes, the skill includes a marketplace feature that allows you to browse, download, and deploy pre-trained templates for tasks like NLP, vision, and regression.

Does this skill require external authentication?

Yes, it requires the Flow Nexus MCP server and a registered account via the flow-nexus CLI to manage sandbox resources.

What neural network architectures are supported?

Flow Nexus supports several core architectures including Feedforward, LSTM, GAN, Autoencoders, and Transformers with customizable layer configurations.

Flow Nexus Neural Networks

Name: Flow Nexus Neural Networks
Author: Microck

byMicrock

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Ciencia de Datos y ML

Trains and deploys distributed neural networks within secure E2B sandbox environments using the Flow Nexus framework.

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Flow Nexus Neural is a comprehensive suite for machine learning orchestration that allows developers to design, train, and manage complex neural architectures directly through Claude. It supports a wide array of model types—including Transformers, LSTMs, and GANs—and leverages distributed E2B sandboxes to scale training across multi-node clusters. Whether you are deploying pre-trained models from the marketplace or building custom federated learning workflows, this skill provides the infrastructure management, benchmarking, and inference tools necessary for professional AI development.

Características Principales

Federated learning capabilities for privacy-preserving data training
Distributed multi-node training clusters with mesh and ring topologies
Pre-built model marketplace for rapid deployment of NLP and Vision templates
Support for Transformer, LSTM, GAN, and Autoencoder architectures
Comprehensive performance benchmarking and validation workflows
81 GitHub stars

Casos de Uso

Deploying scalable image classification clusters with automated worker node management
Building time-series forecasting systems with multi-layered LSTM architectures
Training custom sentiment analysis models using distributed Transformer nodes

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Características Principales

Federated learning capabilities for privacy-preserving data training
Distributed multi-node training clusters with mesh and ring topologies
Pre-built model marketplace for rapid deployment of NLP and Vision templates
Support for Transformer, LSTM, GAN, and Autoencoder architectures
Comprehensive performance benchmarking and validation workflows
81 GitHub stars

Casos de Uso

Deploying scalable image classification clusters with automated worker node management
Building time-series forecasting systems with multi-layered LSTM architectures
Training custom sentiment analysis models using distributed Transformer nodes