MNIST Classifier Trained on a Calculator
π‘See how a non-programmable calculator reaches 98.96% on a tiny MNIST classifier.
β‘ 30-Second TL;DR
What Changed
MNIST digits were binarized and downscaled to 3x3 images, producing nine input features.
Why It Matters
The experiment demonstrates that even extremely constrained hardware can support a useful educational machine-learning workflow. The large gap between the two results also highlights how training data, optimization method, and evaluation design strongly affect reported accuracy.
What To Do Next
Reproduce the experiment with 3x3 binarized MNIST and compare perceptron updates against SGD using balanced class-level precision and recall.
Key Points
- β’MNIST digits were binarized and downscaled to 3x3 images, producing nine input features.
- β’The manually trained bias-free perceptron used six examples and achieved 67.04% validation accuracy.
- β’A separate 1,000-epoch SGD run reached 98.96% validation accuracy on the binary 0-versus-1 task.
- β’The manual model classified all ones correctly but misclassified most zeros.
Weekly AI Recap
Read this week's curated digest of top AI events β
πRelated Updates
Same topic
Explore #perceptron
Same product
More on casio-fx-82ce-x-mnist-classifier
Same source
Latest from Reddit r/MachineLearning
repo2nb 0.2.0 Makes Repo-to-Notebook Sync Easier
AI Boilerplate Cuts ML Setup from Days to Hours
Concise LLM Outputs Cut Costs Without Sacrificing Accuracy
Could Free GPU Compute Help ML Researchers?
AI-curated news aggregator. All content rights belong to original publishers.
Original source: Reddit r/MachineLearning β
This is a summary, not the original. Read the source, or get the weekly briefing.
Weekly AI briefing
One email a week. Unsubscribe anytime.