cybertronai

cybertronai / hinton-problems

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53 implementations of synthetic learning problems from Geoffrey Hinton's experimental papers (1981-2022). Pure numpy, laptop-runnable, paper-comparison metrics per stub.

cybertronai.github.iohinton-problems
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100% credibility
Found May 11, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A NumPy-based catalog reproducing synthetic machine learning problems from Geoffrey Hinton's papers spanning 1981-2022, complete with visualizations, GIFs, and metrics assessing fidelity to original results.

How It Works

1
🔍 Discover Hinton's classic experiments

You hear about a collection of famous AI learning problems from Geoffrey Hinton's papers over 40 years.

2
📂 Pick a problem to explore

Browse the folders, each one a simple puzzle like bouncing balls or adding numbers that AI learns to solve.

3
▶️ Run the example

Click to train the AI on your laptop -- it learns the pattern in minutes with colorful graphs updating live.

4
✨ Watch it succeed

See animated GIFs of the AI getting smarter step by step, matching results from the original papers.

✅ Understand AI history

Compare numbers to Hinton's papers and grasp how these ideas shaped modern AI, all without coding.

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AI-Generated Review

What is hinton-problems?

This repo delivers 53 pure numpy implementations of synthetic learning problems from Geoffrey Hinton's experimental papers spanning 1981-2022, like encoders, bars problems, AIR MultiMNIST, capsules on affNIST, and Forward-Forward on MNIST/CIFAR. Each self-contained stub runs on a laptop CPU in under 5 minutes, spitting out paper-comparison metrics, training curves, weight visualizations, and animated GIFs of learning dynamics. Developers get reproducible baselines to test DL ideas without GPU farms or framework deps.

Why is it gaining traction?

Unlike scattered dl paper implementations github or heavy PyTorch repros, these are laptop-runnable with zero setup—just nix develop or plain Python—focusing on exact metric matches and qualitative repros (27 full, 27 partial). The visual tour and per-problem READMEs make it dead simple to verify against originals, while upcoming ByteDMD instrumentation hooks data-movement benchmarks for post-backprop experiments.

Who should use this?

ML researchers reproducing Hinton's lineage for genai agent implementations github or RAG baselines; backprop skeptics benchmarking Forward-Forward or capsules; students diving into DL history via runnable numpy stubs. Ideal for quick paper-comparison metrics per problem without wrestling torch installs.

Verdict

Grab it for instant historical baselines—20 stars and 1.0% credibility score signal early days, but stellar docs, GIFs, and 55/55 stubs implemented make it fork-worthy now. Maturity lags on GPU-scale reruns, but pure numpy purity shines for CPU tinkering.

(198 words)

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