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Visual Machine Learning contains a set of Machine Learning and Deep Learning interactive visualisation demos for developing intuition. These demos are developed using TensorFlow.js and can be executed directly in your browser. This project is an extension of ML examples from tfjs-examples. We implement new demos, as well as, add additional features into the ones that already existed in TFJS.
Some examples may require web-gl enabled browsers and viewers may experience latency during executing the demos based on the device.
Example name | Demo link | Input data type | Task type | Model type | Training | Inference |
---|---|---|---|---|---|---|
ANN | 🔗 | Iris Dataset | View NN architecture, View Confusion Matrix | Multilayer perceptron | Browser | Browser |
Autoencoder | 🔗 | MNIST dataset | Visualising Latent Space | Autoencoder | Browser | Browser |
Logistic Regression | 🔗 | Various 2D data | Visualising Decision Boundary | Logistic Regression | Browser | Browser |
MNIST-CNN | 🔗 | MNIST | Visualising Activations | CNN | Browser | Browser |
PCA | 🔗 | Various | Visualising Principal Components & projected dimensions | PCA | Browser | Browser |
SVM | 🔗 | 2D Dataset | Visualising Support Vectors and Kernels | SMO | Browser | Browser |
Neural Style Transfer | 🔗 | Image Data | Visualising Style Transfer using MobileNet | Style Transfer | Browser | Browser |
Vanishing Gradients | 🔗 | Iris Dataset | Developing Intuition how Relu Fixes Vanishing Gradients | Neural Networks | Browser | Browser |
All the examples require the following dependencies to be installed.
cd
into the directory
If you are using yarn
:
cd MNIST-CNN
yarn
yarn watch
If you are using npm
:
cd MNIST-CNN
npm install
npm run watch
The convention is that each example contains two scripts:
yarn watch
or npm run watch
: This starts and generates a local development HTML server tracking filesystem for changes, supporting hot-reloading.
yarn build
or npm run build
: generates a dist/
folder which contains the build artifacts and can be used for deployment.
If you want to contribute a demo, please reach out to us on Github issues before sending us a pull request as we are trying to keep this set of examples small and highly curated.