# pkmital/tensorflow_tutorials

From the basics to slightly more interesting applications of Tensorflow

Repository: https://github.com/pkmital/tensorflow_tutorials
Canonical: https://ross.abutalabs.com/products/tensorflow_tutorials
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2021-12-11T20:54:51+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": 3864, "days_push": 1726, "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 5668, forks 1160 (observed 2026-08-28T04:09:27.623852+00:00)

## What it is
A collection of TensorFlow tutorials progressing from basic graph computation to deep learning models like convolutional networks, autoencoders, and residual networks. It includes Python source files and Jupyter notebooks written by Parag K. Mital in 2016.

## Use cases
- learn tensorflow basics from scratch
- tutorial on building a convolutional neural network
- example code for variational autoencoder
- learn linear and logistic regression with tensorflow
- understand residual networks with example code
- hands-on deep learning notebooks

## When to choose
- learning deep learning fundamentals with older TensorFlow 0.x/1.x APIs
- wanting runnable notebook examples of classic architectures like autoencoders and convnets

## When to avoid
- working with modern TensorFlow 2.x or PyTorch
- needing maintained, up-to-date tutorials
- production use of any kind

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: tensorflow, jupyter-notebooks, neural-networks, tutorials, educational

## Member repositories
- pkmital/tensorflow_tutorials (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:27.623852+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 2026-08-29T17:54:18.552695+00:00, confidence not recorded.
  - readme: https://github.com/pkmital/tensorflow_tutorials (fetched 2026-08-28T04:09:27.623852+00:00, sha 7484990fba10)
- Data as of 2026-08-30T08:39:29.467469+00:00.