https://github.com/dsgiitr/visualml
Interactive Visual Machine Learning Demos.
https://github.com/dsgiitr/visualml
autoencoder deep-learning logistic-regression machine-learning mlp-classifier pca style-transfer svm tensorflow-js vanishing-gradient visualizations
Last synced: 2 months ago
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Interactive Visual Machine Learning Demos.
- Host: GitHub
- URL: https://github.com/dsgiitr/visualml
- Owner: dsgiitr
- License: gpl-3.0
- Created: 2020-05-01T13:54:56.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-10T08:08:41.000Z (over 2 years ago)
- Last Synced: 2025-04-15T15:07:57.631Z (2 months ago)
- Topics: autoencoder, deep-learning, logistic-regression, machine-learning, mlp-classifier, pca, style-transfer, svm, tensorflow-js, vanishing-gradient, visualizations
- Language: CSS
- Homepage: http://visualml.dsgiitr.in
- Size: 70.1 MB
- Stars: 114
- Watchers: 8
- Forks: 23
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
-----------------------------------------------------------------------------------------------------------
# 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](https://js.tensorflow.org) and can be executed directly in your browser. This project is an extension of ML examples from [tfjs-examples](https://github.com/tensorflow/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.
- Node.js version 8.9 or higher
- [NPM CLI](https://docs.npmjs.com/cli/npm) OR [Yarn](https://yarnpkg.com/en/)
## How to build?
`cd` into the directory
If you are using `yarn`:
```sh
cd MNIST-CNN
yarn
yarn watch
```
If you are using `npm`:
```sh
cd MNIST-CNN
npm install
npm run watch
```
### Details
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.
## Contributing
If you want to contribute a demo, please reach out to us on
[Github issues](https://github.com/dsgiitr/VisualML/issues)
before sending us a pull request as we are trying to keep this set of examples
small and highly curated.
## Acknowledgements
* [tfjs-examples](https://github.com/tensorflow/tfjs-examples)