Noxer

This package aims to simplify working with a range of AI problems. We keep interfaces of our code as simple as possible while maintaining reasonable flexibility for the extensions of the code.

At current, the following is possible:

1. Supervised learning with deep, recurrent convoneural neural networks with PyTorch backend.
2. Learning generative models such as VAE and GAN's for distributions simple and complex.

What are the benefits of using Noxer

We reuse a lot scikit-learn like interfaces. This yields a few benefits:

• No learning curve to use the code. In fact, you can use all the models same as you do it in sklearn.
• Efficient preprocessing of the data for datasets that fit in memory using FeatureUnion or Pipeline classes. One could possibly go far beyond memory size with dask.
• All machinery necessary for hyperparameter setting and selection. The code can be used directly with GridSearchCV from scikit-learn or better yet with BayesSearchCV from scikit-optimize that is more efficient in number of model trainings.

Installation

Install using pip in terminal:

• If you only want to use code: [sudo] pip install https://github.com/iaroslav-ai/noxer/archive/master.zip
• If you want to edit the code:

git clone https://github.com/iaroslav-ai/noxer.git
cd noxer
sudo pip install -e .

Documentation

Documentation for the code is extracted from docstrings and is located at https://noxer-org.github.io/.

Examples

See example usage in examples folder.

Acknowledgements

Icon made by Freepik from www.flaticon.com .

This software is under construction.