# yusugomori/DeepLearning

Deep Learning (Python, C, C++, Java, Scala, Go)

Repository: https://github.com/yusugomori/DeepLearning
Canonical: https://ross.abutalabs.com/products/yusugomori-deeplearning
Homepage: https://yusugomori.com
Language: Java
License: MIT
License Family: permissive
Topics: deep-learning
Last push: 2021-10-01T16:30:16+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": 4992, "days_push": 1797, "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 3177, forks 1347 (observed 2026-08-28T04:07:47.659267+00:00)

## What it is
A multi-language (Python, C, C++, Java, Scala, Go) educational implementation of classic deep learning algorithms such as Deep Belief Nets, RBMs, denoising autoencoders, MLPs, and CNNs. It accompanies the book 'Java Deep Learning Essentials' and is intended for learning rather than production use.

## Use cases
- learn how deep learning algorithms are implemented from scratch
- study restricted boltzmann machines and autoencoders in plain code
- compare deep learning implementations across python, java, c++, scala and go
- understand backpropagation and dropout without a framework
- reference implementations for a deep learning course or book

## When to choose
- you want readable, framework-free implementations of classic deep learning models
- you are studying deep learning fundamentals in multiple programming languages
- you need code examples matching the Java Deep Learning Essentials book

## When to avoid
- you need production-grade training with GPU acceleration
- you want modern architectures like transformers or a maintained ecosystem
- you need an actively developed library with recent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning
- domain: deep-learning, machine-learning, education
- platform: cross-platform, python, jvm, cpp, c, go
- tags: rbm, autoencoder, dbn, mlp, cnn, educational, multi-language

## Member repositories
- yusugomori/DeepLearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:47.659267+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 2026-08-30T07:25:09.030776+00:00, confidence not recorded.
  - readme: https://github.com/yusugomori/DeepLearning (fetched 2026-08-28T04:07:47.659267+00:00, sha 1e9e9a9aceb4)
  - homepage: https://yusugomori.com (fetched 2026-08-29T09:39:20.737255+00:00, sha ab3a1eb2eddf)
  - site_page: https://yusugomori.com/about (fetched 2026-08-29T09:39:20.746889+00:00, sha b4d17c0d0c2f)
- Data as of 2026-08-30T08:39:29.467469+00:00.