Visual Machine Learning
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.
Overview of Demos
| 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 |
Dependencies
All the examples require the following dependencies to be installed.
How to build?
cd into the directory
If you are using yarn:
cd MNIST-CNN
yarn
yarn watchIf you are using npm:
cd MNIST-CNN
npm install
npm run watchDetails
The convention is that each example contains two scripts:
-
yarn watchornpm run watch: This starts and generates a local development HTML server tracking filesystem for changes, supporting hot-reloading. -
yarn buildornpm run build: generates adist/folder which contains the build artifacts and can be used for deployment.
Contributing
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.