# humphd/have-fun-with-machine-learning

An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks

Repository: https://github.com/humphd/have-fun-with-machine-learning
Canonical: https://ross.abutalabs.com/products/have-fun-with-machine-learning
Language: Python
License: NOASSERTION
License Family: other
Topics: neural-network, image-classification, machine-learning, caffe, tutorial
Last push: 2021-12-19T18:38:53+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": 3534, "days_push": 1718, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5110, forks 530 (observed 2026-08-28T04:09:09.889266+00:00)

## What it is
A hands-on beginner's guide to machine learning and image classification with convolutional neural networks, using Caffe and DIGITS. It walks programmers with no AI background through creating a dataset, training a network, and classifying images like dolphins vs. seahorses.

## Use cases
- learn machine learning as a complete beginner
- train an image classifier with a neural network
- get started with Caffe and DIGITS
- classify images of dolphins vs seahorses
- fine-tune pretrained networks like AlexNet and GoogLeNet
- find a practical ML tutorial without heavy math

## When to choose
- you are a programmer new to machine learning wanting a hands-on walkthrough
- you want to learn image classification using Caffe and DIGITS
- you prefer practitioner-style guides over theory-heavy textbooks

## When to avoid
- you need modern deep learning tooling like PyTorch or TensorFlow
- you want rigorous theoretical explanations of neural networks
- you need actively maintained production-grade software

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, image-processing, deep-learning
- domain: machine-learning, tutorials, computer-vision, education
- platform: python, cross-platform
- tags: tutorial, beginner-friendly, caffe, digits, convolutional-neural-network, image-classification, neural-networks

## Member repositories
- humphd/have-fun-with-machine-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.889266+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 2026-08-29T18:02:09.306745+00:00, confidence not recorded.
  - readme: https://github.com/humphd/have-fun-with-machine-learning (fetched 2026-08-28T04:09:09.889266+00:00, sha 2dff153d8001)
- Data as of 2026-08-30T08:39:29.467469+00:00.