whilo/boltzmann — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2015-10-01
Learn how Restricted Boltzmann Machines work by reading the clean, readable reference implementation.
Train a Deep Belief Network on the MNIST dataset to recognize handwritten digits.
Fine-tune an unsupervised-trained model with backpropagation using labeled data.
Use the optimized high-performance version to process larger real-world datasets.
| whilo/boltzmann | avelino/chrondb | yogthos/flatiron | |
|---|---|---|---|
| Stars | 67 | 97 | 27 |
| Language | Clojure | Clojure | Clojure |
| Last pushed | 2015-10-01 | — | — |
| Maintenance | Dormant | — | — |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 4/5 | 4/5 | 3/5 |
| Audience | researcher | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires familiarity with Clojure and the JVM toolchain to run and modify.
A Clojure deep learning library implementing Boltzmann Machines and related neural networks, with both a readable reference version and an optimized fast version of each algorithm.
Mainly Clojure. The stack also includes Clojure, core.matrix.
Dormant — no commits in 2+ years (last push 2015-10-01).
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
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