This is a Pytorch implementation of the [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://arxiv.org/abs/1711.03705) paper. This algorithm contains a new backpropagation approach called Hedge Backpropagation and its useful for online learning. In this algorithm you model a overnetwork architeture and the algorithm will try to turn on or turn off some of the hidden layers automatically. This algorithm uses the first hidden layer to train/predict but if it is going bad it starts to use another layers automatically. For more informations read the paper in the 'References' section.
## Installing
```
pip install onn
```
## New features
- The algortihm works with batch now. (It is not recommended because this is an online approach. It is useful for experimentation.)
- The algorithm can use CUDA if available. (If the network is very small, it is not recommended. The CPU will process more fast.)
## Contributors
-[Alison de Andrade Carrera](https://github.com/alison-carrera)
-[Fábio Silva Vilas Boas](https://github.com/fabiosvb)
