# bnsreenu/python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

Repository: https://github.com/bnsreenu/python_for_microscopists
Canonical: https://ross.abutalabs.com/products/python_for_microscopists
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-04-21T20:22:30+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 78, release rhythm 35, longevity 100
- inputs: {"age_days": 2641, "days_push": 134, "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 4408, forks 2498 (observed 2026-08-28T04:08:48.436567+00:00)

## What it is
A collection of Jupyter notebooks supporting the DigitalSreeni YouTube tutorials, teaching Python from basics through advanced machine learning and deep learning with an emphasis on image processing. It is educational material, not a software library.

## Use cases
- learn python for image analysis
- segment microscopy images with deep learning
- fine-tune detectron2 for instance segmentation
- learn image processing with python
- follow deep learning tutorials with notebooks

## When to choose
- you want tutorial-style notebooks to learn image analysis and deep learning
- you work with microscopy or biological imaging data
- you prefer learning by following along with YouTube videos

## When to avoid
- you need a production-ready library or package to install
- you need maintained, tested code for a real application
- you want a standalone tool without video context

## Facets
- artifact type: learning-resource
- maturity: active
- function: image-processing, machine-learning, deep-learning, computer-vision
- domain: tutorials, image-processing, machine-learning, deep-learning, computer-vision
- platform: python
- tags: jupyter-notebooks, microscopy, image-segmentation, youtube-tutorials, digital-sreeni

## Member repositories
- bnsreenu/python_for_microscopists (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.436567+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 2026-08-29T18:21:04.641662+00:00, confidence not recorded.
  - readme: https://github.com/bnsreenu/python_for_microscopists (fetched 2026-08-28T04:08:48.436567+00:00, sha aeb860573913)
- Data as of 2026-08-30T08:39:29.467469+00:00.