# Newmu/Theano-Tutorials

Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.

Repository: https://github.com/Newmu/Theano-Tutorials
Canonical: https://ross.abutalabs.com/products/theano-tutorials
Language: Python
License: MIT
License Family: permissive
Last push: 2015-12-07T02:08:37+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": 4362, "days_push": 3923, "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 1307, forks 437 (observed 2026-08-28T04:04:19.101033+00:00)

## What it is
A collection of bare-bones Python tutorials introducing machine learning with Theano, progressing from linear regression to convolutional neural networks. It uses the MNIST dataset and includes a script to download it automatically.

## Use cases
- learn deep learning basics with Theano
- understand how neural networks are implemented from scratch
- tutorial on linear regression to CNNs
- example code for building MLPs in Theano
- learn MNIST classification step by step

## When to choose
- you want minimal, readable Theano code for learning ML fundamentals
- you are studying how classic neural network layers are implemented

## When to avoid
- you need a maintained framework - Theano is discontinued
- you want production deep learning - use PyTorch or TensorFlow instead

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials
- platform: python
- tags: theano, tutorial, mnist, neural-networks, linear-regression, convolutional-neural-networks

## Member repositories
- Newmu/Theano-Tutorials (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:19.101033+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 2026-08-30T04:50:07.891028+00:00, confidence not recorded.
  - readme: https://github.com/Newmu/Theano-Tutorials (fetched 2026-08-28T04:04:19.101033+00:00, sha 358dc12d4c76)
- Data as of 2026-08-30T08:39:29.467469+00:00.