flow-nexus-neural

---
name: flow-nexus-neural
description: Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
version: 1.0.0
category: ai-ml
tags:
  - neural-networks
  - distributed-training
  - machine-learning
  - deep-learning
  - flow-nexus
  - e2b-sandboxes
requires_auth: true
mcp_server: flow-nexus
---

# 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

```bash
# Add Flow Nexus MCP server
claude mcp add flow-nexus npx flow-nexus@latest mcp start

# Register and login
npx flow-nexus@latest register
npx flow-nexus@latest login
```

## 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

```javascript
mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "feedforward",
      layers: [
        { type: "dense", units: 256, activation: "relu" },
        { type: "dropout", rate: 0.3 },
        { type: "dense", units: 128, activation: "relu" },
        { type: "dropout", rate: 0.2 },
        { type: "dense", units: 64, activation: "relu" },
        { type: "dense", units: 10, activation: "softmax" }
      ]
    },
    training: {
      epochs: 100,
      batch_size: 32,
      learning_rate: 0.001,
      optimizer: "adam"
    },
    divergent: {
      enabled: true,
      pattern: "lateral", // quantum, chaotic, associative, evolutionary
      factor: 0.5
    }
  },
  tier: "small",
  user_id: "your_user_id"
})
```

#### Example: LSTM for Time Series

```javascript
mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "lstm",
      layers: [
        { type: "lstm", units: 128, return_sequences: true },
        { type: "dropout", rate: 0.2 },
        { type: "lstm", units: 64 },
        { type: "dense", units: 1, activation: "linear" }
      ]
    },
    training: {
      epochs: 150,
      batch_size: 64,
      learning_rate: 0.01,
      optimizer: "adam"
  

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