# lilipads/gradient_descent_viz

interactive visualization of 5 popular gradient descent methods with step-by-step illustration and hyperparameter tuning UI

Repository: https://github.com/lilipads/gradient_descent_viz
Canonical: https://ross.abutalabs.com/products/gradient_descent_viz
Language: C++
License: MIT
License Family: permissive
Last push: 2024-08-04T02:38:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2285, "days_push": 760, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1409, forks 159 (observed 2026-08-28T04:04:38.655387+00:00)

## What it is
A cross-platform C++/Qt desktop application that interactively visualizes five popular gradient descent optimization methods (vanilla GD, momentum, AdaGrad, RMSProp, Adam). It offers multiple loss surfaces, hyperparameter tuning, step-by-step animations, and visual elements to build intuition about how each optimizer behaves.

## Use cases
- visualize how adam and rmsprop handle saddle points
- understand the difference between adagrad and rmsprop
- tune learning rate to see effect on gradient descent
- learn gradient descent optimizers interactively
- teaching tool for machine learning optimization methods
- see step-by-step calculation of momentum descent

## When to choose
- you want an interactive, visual way to build intuition about gradient descent optimizers
- you are teaching or learning machine learning optimization concepts
- you want to experiment with hyperparameters on different loss surfaces

## When to avoid
- you need to run actual optimizer experiments on real datasets or models
- you need a library to embed in your own training code
- you require a web-based or scriptable visualization

## Facets
- artifact type: application
- maturity: maintenance
- function: data-visualization, machine-learning, gui, simulation
- domain: machine-learning, education, data-visualization, desktop-applications
- platform: windows, cpp, cross-platform
- tags: gradient-descent, optimization, qt, interactive-visualization, adam, momentum, adagrad, rmsprop, learning-tool, macos, desktop

## Member repositories
- lilipads/gradient_descent_viz (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.655387+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 2026-08-30T04:38:32.791972+00:00, confidence not recorded.
  - readme: https://github.com/lilipads/gradient_descent_viz (fetched 2026-08-28T04:04:38.655387+00:00, sha bc9754466f49)
- Data as of 2026-08-30T08:39:29.467469+00:00.