# amaas/stanford_dl_ex

Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial

Repository: https://github.com/amaas/stanford_dl_ex
Canonical: https://ross.abutalabs.com/products/stanford_dl_ex
Homepage: http://ufldl.stanford.edu/tutorial
License: MIT
License Family: permissive
Last push: 2021-05-12T19:51:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4726, "days_push": 1939, "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 2623, forks 1577 (observed 2026-08-28T04:07:05.169252+00:00)

## What it is
Starter code and programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning (UFLDL) Tutorial. It accompanies the tutorial by Andrew Ng and colleagues, letting learners implement feature learning and deep learning algorithms themselves.

## Use cases
- learn deep learning by implementing algorithms from scratch
- work through the Stanford UFLDL tutorial exercises
- practice unsupervised feature learning with starter code
- study autoencoders and sparse coding hands-on
- supplement a machine learning course with coding exercises

## When to choose
- you want to understand deep learning fundamentals by coding them yourself
- you are following the Stanford UFLDL tutorial and need the starter code
- you prefer classic, math-focused exercises over modern framework-based tutorials

## When to avoid
- you want production-ready deep learning code or a maintained library
- you prefer learning with modern frameworks like PyTorch or TensorFlow
- you need up-to-date course material - the tutorial and exercises are dated

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: deep-learning, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: exercise-code, unsupervised-feature-learning, autoencoders, starter-code, tutorial-companion

## Member repositories
- amaas/stanford_dl_ex (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:05.169252+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 2026-08-30T02:19:54.255795+00:00, confidence not recorded.
  - readme: https://github.com/amaas/stanford_dl_ex (fetched 2026-08-28T04:07:05.169252+00:00, sha 6231da6d1855)
  - homepage: http://ufldl.stanford.edu/tutorial (fetched 2026-08-29T10:03:17.317020+00:00, sha f59a84720960)
- Data as of 2026-08-30T08:39:29.467469+00:00.