import React from 'react'; import { Book, MousePointer2, Sparkles, Code2, ShieldCheck, Zap, ChevronRight, Info, Layers, Cpu, Terminal, ArrowLeft, CheckCircle, XCircle } from 'lucide-react'; interface HelpPageProps { onBack: () => void; } const HelpPage: React.FC = ({ onBack }) => { const handleScrollTo = (id: string) => { const element = document.getElementById(id); if (element) { element.scrollIntoView({ behavior: 'smooth' }); } }; return (
{/* Header */}
Documentation
{/* Spacer for centering */}
{/* Left Content Area */}
{/* Intro */}

Mastering
Neural Architectures

Welcome to the definitive guide for Architecture Agents. Our platform empowers you to design, validate, and export production-ready deep learning models using a combination of visual intuition and state-of-the-art AI assistance.

{/* Quick Start Guide */}

Quick Start Guide

{/* Advanced Patterns */}

Advanced Design Patterns

Residual & Skip Connections

To prevent vanishing gradients in deep networks, use skip connections. In Architecture Agents, you can easily create these by branching an output to multiple inputs. The generated code will handle the addition or concatenation logic based on your configuration.

ResNet DenseNet U-Net

Attention Mechanisms

Modern architectures rely heavily on attention. Use our 'MultiheadAttention' and 'SelfAttention' nodes to build Transformer-based models. Ensure your hidden dimensions are divisible by the number of heads to maintain mathematical consistency.

Self-Attention Cross-Attention Flash Attention
{/* Prompting Tips */}

AI Prompting Strategies

The AI Builder is a powerful co-pilot. To get the most accurate architectures, treat your prompts like technical specifications.

High-Quality Prompts

  • Structural Detail: "Design a 5-layer CNN with increasing filter sizes (32, 64, 128, 256, 512) and batch normalization after each conv."
  • Functional Requirements: "Create a sequence-to-sequence model using bidirectional LSTMs and an attention-based decoder for translation."

Common Pitfalls

  • Ambiguous Terms: Avoid "Make it fast" or "Make it deep". Instead, use "Reduce parameters" or "Add 10 residual blocks".
  • Mixing Domains: Don't ask for training code and architecture in the same prompt. Focus on the graph structure first.
{/* Horizontal Ad Slot / Featured Resource */}
Featured Resource

Accelerate Your Training with Cloud GPUs

Once you've designed your architecture, you'll need the compute power to train it. Explore our recommended cloud partners for seamless PyTorch deployment.

Partner content and specialized developer offers appear here.

{/* Model Use Cases */}

Model Taxonomy & Use Cases

{/* FAQ */}

Frequently Asked Questions

{/* Glossary */}

Glossary of Terms

{/* Best Practices */}

Architecture Best Practices

1. Start Simple

Begin with a baseline model (like a simple MLP or a standard ResNet) before adding complexity. Use the AI Builder to generate these baselines quickly.

2. Monitor Dimensions

Pay close attention to the output shapes of your layers. Convolutional layers reduce spatial dimensions unless padding is used. Ensure your final layer matches the number of classes in your dataset.

3. Use Normalization

Include BatchNorm or LayerNorm after convolutional or linear layers. This helps stabilize the learning process and allows for higher learning rates.

{/* Keyboard Shortcuts */}

Keyboard Shortcuts

Action Shortcut
{/* Troubleshooting */}

Troubleshooting

Dimension Mismatch Errors

If you see a red connection line, it means the output dimension of the source node does not match the input dimension of the target node. Use a Linear layer or AdaptivePooling to resize the data.

Graph is Disconnected

All nodes must be reachable from the Input node and eventually lead to an Output node. Check for isolated nodes that might be breaking the flow.

Exported Code Fails to Run

Ensure you have torch installed in your environment. The generated code assumes a standard PyTorch setup. If a specific layer is missing, check the Custom Layer configuration.

{/* Call to Action */}

Ready to start building?

Join thousands of developers using Architecture Agents to design the next generation of AI models.

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© {new Date().getFullYear()} wuhp.org. All rights reserved.

); }; const GlossaryItem: React.FC<{ term: string, definition: string }> = ({ term, definition }) => (
{term}

{definition}

); const GuideStep: React.FC<{ number: string, title: string, description: string }> = ({ number, title, description }) => (
{number}

{title}

{description}

); const UseCaseCard: React.FC<{ title: string, useCase: string, description: string }> = ({ title, useCase, description }) => (

{title}

{useCase}

{description}

); const ShortcutRow: React.FC<{ action: string, shortcut: string }> = ({ action, shortcut }) => ( {action} {shortcut} ); const FeatureSection: React.FC<{ icon: React.ReactNode, title: string, description: string, tips: string[] }> = ({ icon, title, description, tips }) => (
{icon}

{title}

{description}

Pro Tips

    {tips.map((tip, i) => (
  • {tip}
  • ))}
); const FAQItem: React.FC<{ question: string, answer: string }> = ({ question, answer }) => { const [isOpen, setIsOpen] = React.useState(false); return (
{isOpen && (
{answer}
)}
); }; const ChevronDown = ({ size, className }: { size: number, className?: string }) => ( ); export default HelpPage;