# matsuolab-edu/dl4us

Repository: https://github.com/matsuolab-edu/dl4us
Canonical: https://ross.abutalabs.com/products/dl4us
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2023-10-17T11:41:50+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2710, "days_push": 1051, "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 1234, forks 247 (observed 2026-08-28T04:04:04.676795+00:00)

## What it is
DL4US is a publicly released set of Jupyter Notebook course materials for a practical deep learning course aimed at engineers, originally offered as an online class. It covers seven lessons spanning machine learning basics, CNNs, RNNs, neural translation, image captioning, generative models (GAN/VAE), and reinforcement learning, all implemented with Keras/TensorFlow.

## Use cases
- learn deep learning from scratch as an engineer
- study CNNs and image classification with Keras
- understand RNNs, LSTM, and seq2seq translation models
- learn how GANs and VAEs generate images
- get started with reinforcement learning and DQN using OpenAI Gym
- follow hands-on Jupyter Notebook exercises for neural networks
- practice building image captioning models with pretrained models

## When to choose
- you want a structured, lesson-by-lesson deep learning curriculum in notebook form
- you prefer learning with Keras/TensorFlow high-level APIs
- you want coverage from fundamentals through GANs and reinforcement learning in one course
- you want free materials runnable locally or on Google Colaboratory

## When to avoid
- you need PyTorch-based tutorials
- you want an actively maintained course with support or homework grading
- you need production-ready code rather than educational examples
- you cannot read Japanese, as the materials are written in Japanese

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, nlp, computer-vision, reinforcement-learning
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, cross-platform
- tags: jupyter-notebooks, keras, tensorflow, course-material, japanese, gan, seq2seq, dqn, google-colab, natural-language-processing, web-server

## Member repositories
- matsuolab-edu/dl4us (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.676795+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 2026-08-30T08:22:10.888975+00:00, confidence not recorded.
  - readme: https://github.com/matsuolab-edu/dl4us (fetched 2026-08-28T04:04:04.676795+00:00, sha 2166c6aa7981)
- Data as of 2026-08-30T08:39:29.467469+00:00.