yusugomori/DeepLearning

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3177 stars · 1347 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity maintenance

deep-learning machine-learning deep-learning machine-learning education cross-platform python jvm cpp c go rbm autoencoder dbn mlp cnn educational multi-language

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