What it does OpenNN is a C++ library for building and training neural networks. It targets “advanced analytics” use cases—customer segmentation, churn prediction, predictive maintenance—without wrapping Python or dragging in heavy dependencies. The project provides CMake and Qt Creator build files, plus tutorials and examples hosted on its own site.

The interesting bit The README insists the library “does not make use of the Qt library” despite shipping .pro files for Qt Creator—a small but honest clarification that suggests the authors have been burned by dependency bloat before. The focus on raw execution speed and memory allocation, rather than ease-of-use or ecosystem, marks this as an old-school C++ project.

Key highlights

• Pure C++ implementation with no Python layer
• CMake and Qt Creator IDE support out of the box
• Parallelized and “constantly optimized” (per the README; no specific benchmarks cited)
• Documentation and tutorials maintained at opennn.net
• Developed by Artelnics, a commercial AI consultancy

Caveats

• The Travis CI badge appears to use a broken template ({ORG-or-USERNAME}/{REPO-NAME}), suggesting maintenance has lapsed
• No version numbers, release notes, or contribution guidelines visible in the README
• “High performance” claims are stated, not demonstrated; no comparison benchmarks provided

Verdict Worth a look if you’re embedding neural nets in a C++ codebase where Python overhead is unacceptable, or need tight control over memory. Skip it if you want a thriving community, PyTorch interoperability, or modern MLOps tooling.