Handwritten Digit Playground Notes: Bringing an Offline Classifier into the Browser
AI Learning Project
Handwritten Digit Playground Notes: Bringing an Offline Classifier into the Browser
Notes on the browser-side handwritten digit module, including the compact softmax demo model, sample browsing, drawing input, and probability inspection.
AI Learning Project

Handwritten Digit Playground Notes: Bringing an Offline Classifier into the Browser

Reading info

Level: Practice Reading time: 8 min
  • Browser Demo
  • Softmax
  • Visualization
Open knowledge map

After adding the handwritten digit project to the site, I did not want the playground to retrain all 42000 samples inside the browser. That would be too heavy, especially on phones. The current setup is more practical: keep the full offline C project as a download, and add a lightweight interactive browser model for immediate experimentation.

That split keeps the system honest. Visitors do not need a local compiler just to understand the input and prediction flow, and the offline project can stay focused on the actual implementation instead of being distorted to fit a front-end demo.

1. What the handwritten digit module does

The new playground tab supports three main actions:

  • Browse real samples loaded from the training set into a 28 by 28 grid
  • Draw your own input directly in the browser and run a prediction
  • Inspect class probabilities for all ten digits instead of only showing one final label

This is useful for learning because you can see not only what the model predicts, but also which competing digits remain plausible.

2. How the browser model relates to the C project

The page does not load the full training loop. It loads a compact set of pre-trained softmax weights. That keeps the browser demo in the same model family as the C project while making the interaction fast enough for normal page use.

The current browser demo weights come from a smaller training split:

  • training samples: 10000
  • validation samples: 2000
  • epochs: 18
  • learning rate: 0.35
  • validation accuracy: about 90.65%

The goal is not to chase the strongest possible score. The goal is to make the model response immediate and interpretable in the browser while preserving the full downloadable project separately.

3. Why the browser does not retrain the whole project

The reasons are straightforward:

  • the full dataset is too large to reprocess on every page load
  • repeated gradient updates in the browser would hurt interaction quality
  • mobile users need a stable experience more than a heavy in-page training loop

So the playground is better used as a visualization and interaction surface, not as a replacement for the offline training environment.

4. The best way to use the module

A good sequence is:

  1. load a real sample and confirm that the grid and prediction agree
  2. edit a few strokes and watch the probability distribution change
  3. clear the board and draw a digit yourself to see where the model hesitates

If you draw a 9 and the model also gives meaningful probability to 4 or 7, that is not a failure of the UI. It is a useful view into the model boundary.

5. How it fits with the posts and downloads

The handwritten digit content is now structured as one connected set:

  • post one explains the dataset files and the 784-dimensional input format
  • post two explains the C softmax classifier, training loop, and exported predictions
  • the downloads page collects the source, compressed datasets, sample submission, final submission, and browser model file
  • the playground provides the interactive lightweight version

That way, readers can understand the logic in the posts, grab the files from downloads, and try the behavior immediately in the playground.

6. What is worth improving next

This version is already useful for teaching, but there are clear next steps:

  • add input helpers such as brush-size controls or centering logic
  • collect misclassified examples into a browsable error set
  • compare the browser demo outputs against the offline C project in more detail

For a learning-oriented technical site, those improvements matter more than extra decoration. The point is to make the relationship between input, model, and output easier to study.

7. Where to continue

If you have not read the implementation post yet, continue with the C classifier article. If you want to run the full project locally, go straight to the downloads page. If you just want to experiment right away, open the playground and start drawing digits.

Internal links

Related tutorials

Review foundation Handwritten Digit Softmax in C

Follow the C implementation from logits and softmax probabilities to confusion matrices and submission export.

 Continue next CIFAR-10 Tiny CNN Tutorial in C

Build and train a small convolutional neural network for CIFAR-10 image classification, then read its loss and accuracy output.

 Learning path AI Learning Path

Read AI foundations, machine learning, CNN work, traffic defense, and AI security in order.

 Resources Resource Library

Download companion code, datasets, diagrams, model files, and notes.

 Topic hub Student AI Projects

Connect handwritten digits, browser demos, and CNN projects into a portfolio path.

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FAQ

Who is this article for?

This article is for readers who want a practice-level guide to Handwritten Digit Playground Notes. It takes about 8 min and focuses on Browser Demo, Softmax, Visualization.

What should I read next?

The recommended next step is CIFAR-10 Tiny CNN Tutorial in C, so the article connects into a longer learning route instead of ending as an isolated note.

Does this article include runnable code or companion resources?

Yes. Use the run notes, resource cards, and download links on the page to reproduce the example or inspect the companion files.

How does this article fit into the larger site?

It is connected to the article context block, learning routes, resources, and project timeline so readers can move from concept to implementation.

Article context

AI Learning Project

A practical route from AI concepts to machine learning workflow, evaluation, neural networks, Python practice, handwritten digits, a CIFAR-10 CNN, adversarial traffic-defense notes, and AI security.

Level: Practice Reading time: 8 min
  • Browser Demo
  • Softmax
  • Visualization

Your next step

Continue: CIFAR-10 Tiny CNN Tutorial in C
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Other language version 手写数字实验记录：怎么把离线分类项目接进浏览器实验台
Share summary Handwritten Digit Playground Notes

See how the offline classifier was adapted into a browser demo with drawing input and probability output.

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Project timeline

Published posts

  1. AI Basics Learning Roadmap Separate AI, machine learning, and deep learning before going into implementation details.
  2. Machine Learning Workflow Follow the practical path from data and features to training, prediction, and evaluation.
  3. Model Training and Evaluation Understand loss, overfitting, train/test splits, accuracy, recall, and F1.
  4. Neural Network Basics Move from perceptrons to activation, forward propagation, backpropagation, and training loops.
  5. NLP Basics: Understanding Bag of Words and TF-IDF An introduction to the most fundamental text representation methods in NLP: Bag of Words (BoW) and TF-IDF.
  6. RNN Basics: Handling Sequential Data with Memory Understand the core concepts of Recurrent Neural Networks (RNN), the role of hidden states, and their application in NLP.
  7. Transformer Self-Attention Read Q/K/V, scaled dot-product attention, multi-head attention, and positional encoding before exploring LLM internals.
  8. Python AI Mini Practice Run a small scikit-learn classification task and read the experiment output.
  9. Handwritten Digit Dataset Basics Read train.csv, test.csv, labels, and the flattened 28 by 28 pixel layout before training the classifier.
  10. Handwritten Digit Softmax in C Follow the C implementation from logits and softmax probabilities to confusion matrices and submission export.
  11. Handwritten Digit Playground Notes See how the offline classifier was adapted into a browser demo with drawing input and probability output.
  12. CIFAR-10 Tiny CNN Tutorial in C Build and train a small convolutional neural network for CIFAR-10 image classification, then read its loss and accuracy output.
  13. Building a Tiny CIFAR-10 CNN in C: Convolution, Pooling, and Backpropagation A source-based walkthrough of cifar10_tiny_cnn.c, covering CIFAR-10 binary input, 3x3 convolution, ReLU, max pooling, fully connected logits, softmax, backpropagation, and local commands.
  14. High-Entropy Traffic Defense Notes Study encrypted metadata leaks, entropy, traffic classifiers, and a defensive Python chaffing prototype.
  15. AI Security Threat Modeling Build a defense map with NIST adversarial ML, MITRE ATLAS, and OWASP LLM risks.
  16. Adversarial Examples and Robust Evaluation Evaluate clean and perturbed accuracy with an FGSM-style digits experiment.
  17. Data Poisoning and Backdoor Defense Study poison rate, trigger behavior, attack success rate, and training pipeline controls.
  18. Model Privacy and Extraction Defense Measure membership inference signal and surrogate fidelity against a local toy model.
  19. LLM, RAG, and Agent Security Separate instructions from data and enforce tool permissions against indirect prompt injection.

Published resources

  1. Python AI practice code guide The article includes a runnable scikit-learn classification script.
  2. digit_softmax_classifier.c The C source for the handwritten digit softmax classifier.
  3. train.csv.zip Compressed handwritten digit training set with 42000 labeled samples.
  4. test.csv.zip Compressed handwritten digit test set with 28000 unlabeled samples.
  5. sample_submission.csv The official submission format example for checking the final output columns.
  6. submission.csv The prediction file generated by the current C project.
  7. digit-playground-model.json The compact softmax demo model and sample set used by the browser playground.
  8. digit-sample-grid.svg A small handwritten digit preview grid extracted from the training set.
  9. Handwritten digit project bundle Contains the source file, compressed datasets, submission files, browser model, and preview grid.
  10. cifar10_tiny_cnn.c source Single-file C tiny CNN with CIFAR-10 loading, convolution, pooling, softmax, and backpropagation.
  11. model_weights.bin sample weights Model weights generated by one local small-sample run.
  12. test_predictions.csv sample predictions Sample test prediction output from the CIFAR-10 tiny CNN.
  13. CNN project explanation PDF Companion explanation material for the CNN project.
  14. Virtual Mirror redacted code skeleton A redacted mld_chaffing_v2.py control-flow skeleton with secrets, node topology, and target lists removed.
  15. Virtual Mirror stress-test template A redacted CSV template for CPU, memory, peak threads, pulse rate, latency, and error measurements.
  16. Virtual Mirror classifier-evaluation template A CSV template for TP, FN, FP, TN, accuracy, precision, recall, F1, ROC-AUC, entropy, and JS divergence.
  17. Virtual Mirror resource notes Notes explaining why the public resources include only redacted code, test templates, and architecture context.
  18. AI Security Lab README Setup, safety boundaries, and quick-run commands for the AI Security series.
  19. AI Security Lab full bundle Includes safe toy scripts, result CSVs, risk register, attack-defense matrix, and architecture diagram.
  20. AI security risk register CSV risk register template for AI threat modeling and release review.
  21. AI attack-defense matrix Maps attack surface, toy demo, metric, and defensive control into one CSV table.
  22. AI Security Lab architecture diagram Shows threat modeling, robustness, data integrity, model privacy, and RAG guardrails.
  23. FGSM digits robustness script FGSM-style perturbation and accuracy-drop experiment for a local digits classifier.
  24. Data poisoning and backdoor toy script Demonstrates poison rate, trigger behavior, and attack success rate on digits.
  25. Model privacy and extraction toy script Outputs membership AUC, target accuracy, surrogate fidelity, and surrogate accuracy.
  26. RAG prompt injection guard toy script Uses a deterministic toy agent to demonstrate external-data demotion and tool-policy blocking.
  27. Deep Learning topic share card A 1200x630 SVG card for sharing the Deep Learning / CNN topic hub.
  28. Machine Learning From Scratch share card A 1200x630 SVG card for the K-means, Iris, and ML workflow topic hub.
  29. Student AI Projects share card A 1200x630 SVG card for handwritten digits, C classifiers, and browser demos.
  30. CNN convolution scan animation An 8-second Remotion animation showing how a 3x3 convolution kernel scans an input and builds a feature map.

Current route

  1. AI Basics Learning Roadmap Learning path step
  2. Machine Learning Workflow Learning path step
  3. Model Training and Evaluation Learning path step
  4. Neural Network Basics Learning path step
  5. Transformer Self-Attention Learning path step
  6. LLM Visualizer Learning path step
  7. Python AI Mini Practice Learning path step
  8. Handwritten Digit Dataset Basics Learning path step
  9. Handwritten Digit Softmax in C Learning path step
  10. Handwritten Digit Playground Notes Learning path step
  11. CIFAR-10 Tiny CNN Tutorial in C Learning path step
  12. High-Entropy Traffic Defense Notes Learning path step
  13. AI Security Threat Modeling Learning path step
  14. Adversarial Examples and Robust Evaluation Learning path step
  15. Data Poisoning and Backdoor Defense Learning path step
  16. Model Privacy and Extraction Defense Learning path step
  17. LLM, RAG, and Agent Security Learning path step

Next notes

  1. Add more image-classification and error-analysis cases
  2. Turn common metrics into a quick reference
  3. Add more AI security defense experiment notes