nn

Neural Network package for Torch, enabling deep learning.

DecliningOpen SourceLow lock-in

Pricing

Free tier

Flat rate

Adoption

Cooling

License

Open Source

Data freshness

Aging · Jun 8, 2026

Overview

What is nn?

The nn package is a comprehensive library for building and training neural networks within the Torch framework. It provides essential components like layers, loss functions, and optimizers to facilitate efficient deep learning model development.

Key differentiator

nn stands out as a robust and flexible library for deep learning, tightly integrated with the Torch ecosystem to provide efficient computation.

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Comprehensive library for neural network componentsmedium

Integration with Torch for efficient computationmedium

Supports various deep learning architectures and modelsmedium

↓ Weaknesses

Steep learning curve for non-Lua developershigh

The nn package is primarily designed with Lua, which may be unfamiliar to many modern deep learning practitioners who are more accustomed to Python or other languages.

Limited community and support due to niche language choicemedium

Lua is less popular compared to Python in the machine learning space, leading to a smaller user base and fewer resources for troubleshooting and learning.

Integration limitations with modern deep learning frameworkshigh

The nn package integrates primarily with Torch, which is not as widely used or supported as TensorFlow or PyTorch in the current landscape of deep learning tools.

Documentation and examples are sparse compared to popular alternativesmedium

Official documentation for nn package lacks comprehensive tutorials and example use cases, making it harder for new users to get started effectively.

Fit analysis

Who is it for?

✓ Best for

Developers working on deep learning projects who need a comprehensive library for building and training neural networks.

Researchers looking to experiment with different neural network architectures within the Torch framework.

✕ Not a fit for

Teams requiring cloud-based managed services for model deployment

Projects that require real-time inference without local setup

Cost structure

Pricing

Free Tier

Available

Open source — free to use

Starts at

$0

Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Ecosystem

Relationships

Next step

Get Started with nn

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →