# novak-99/MLPP

A library created to revitalize C++ as a machine learning front end. Per aspera ad astra.

Repository: https://github.com/novak-99/MLPP
Canonical: https://ross.abutalabs.com/products/mlpp
Language: C++
License: MIT
License Family: permissive
Topics: cpp, data-science, machine-learning, deep-learning
Last push: 2022-02-25T06:52:46+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1927, "days_push": 1650, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1108, forks 152 (observed 2026-08-28T04:03:37.058559+00:00)

## What it is
ML++ is a C++ machine learning library providing regression models, dynamically sized neural networks, statistics, and optimization algorithms. It aims to make C++ a viable front end for machine learning by bridging low-level development and ML engineering.

## Use cases
- train machine learning models in c++
- build neural networks with c++
- linear and logistic regression in c++
- implement gradient descent from scratch
- statistics and linear algebra for c++ data science
- avoid python for machine learning workloads

## When to choose
- you need ML in a C++ codebase or performance-critical native application
- you want a lightweight dependency with no Python runtime
- you are learning ML internals and want readable C++ implementations

## When to avoid
- you need a mature production ecosystem with GPU acceleration and tooling like PyTorch
- you want transformers, LLMs, or modern deep learning architectures
- you need frequent updates and large community support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, data-science, math
- domain: machine-learning, deep-learning, data-science
- platform: cpp, cross-platform
- tags: header-only-style, neural-networks, regression, statistics, gradient-descent, c++17, linux, macos

## Member repositories
- novak-99/MLPP (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:37.058559+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:43:50.503466+00:00, confidence not recorded.
  - readme: https://github.com/novak-99/MLPP (fetched 2026-08-28T04:03:37.058559+00:00, sha d17d4832b125)
- Data as of 2026-08-30T08:39:29.467469+00:00.