Modern deep learning architectures and tasks, all implemented in Keras
Have you ever been in a situation where you scroll through forums, reddit, journal posts or even 5 year old github repos trying to find an example for a Deep Learning task that you want to implement using Python and Keras?
Is your code not working because the original code was outdated?
Are you tired of always seeing the same benchmark datasets without the possibility of implementing your own?
Do you like to contribute to the spread of Deep Learning and want to implement your own examples in Keras?
Well, then Do it in Keras is for you!
What is Do it in Keras?
Do it in Keras is a collection of easy to use Jupyter Notebooks hosted in Github which have various implementations of Deep Learning with custom datasets and future exercises for you to learn the most popular Deep Learning library: Keras.
How do I contribute to it?
Do it in Keras is now participating in Hacktoberfest, so you can not only learn a lot more about Keras and Open Source, but you can also win a lot of cool prizes!
If you've never heard about Hacktoberfest, you can check it out here.
To contribute to Do it in Keras, you only need to make a pull request on the official Do it in Keras repository. Anything related to Deep Learning with Keras helps, and you can also upload your own implementation of a Deep Learning task.
Top comments (0)