# sjchoi86/dl_tutorials

Deep learning tutorials (2nd ed.)

Repository: https://github.com/sjchoi86/dl_tutorials
Canonical: https://ross.abutalabs.com/products/dl_tutorials
License: MIT
License Family: permissive
Last push: 2016-07-26T16:31:07+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": 3732, "days_push": 3690, "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 1515, forks 392 (observed 2026-08-28T04:04:56.490743+00:00)

## What it is
A collection of deep learning tutorial presentations and accompanying code (2nd edition), covering topics from Python and TensorFlow basics to CNNs, semantic segmentation, and autoencoders. It is structured as a multi-week course with slides and MNIST-based implementation examples.

## Use cases
- learn deep learning fundamentals from scratch
- find tutorial slides explaining CNNs and AlexNet
- get example code implementing MLPs and CNNs on MNIST
- study semantic segmentation implementations like FCN and DeepLab
- learn TensorFlow basics with hands-on examples
- understand regularization and optimization methods in deep learning

## When to choose
- you want structured, week-by-week deep learning course material with slides
- you are learning TensorFlow 1.x-era deep learning with MNIST examples
- you need introductory explanations of classic architectures like AlexNet, GoogLeNet, and autoencoders

## When to avoid
- you need up-to-date material covering modern frameworks like PyTorch or current architectures
- you want a maintained library or production-ready code
- you need tutorials on transformers or large language models

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning, computer-vision
- domain: deep-learning, machine-learning, computer-vision, tutorials
- platform: python
- tags: tutorials, tensorflow, mnist, cnn, presentations, educational

## Member repositories
- sjchoi86/dl_tutorials (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:56.490743+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 2026-08-30T04:32:10.614575+00:00, confidence not recorded.
  - readme: https://github.com/sjchoi86/dl_tutorials (fetched 2026-08-28T04:04:56.490743+00:00, sha fa5e0b01e095)
- Data as of 2026-08-30T08:39:29.467469+00:00.