This is an open solution to the TalkingData Challenge.

Deliver open source, ready-to-use and extendable solution to this competition. This solution should - by itself - establish solid benchmark, as well as provide good base for your custom ideas and experiments.

1. clone this repository: git clone https://github.com/neptune-ml/open-solution-talking-data.git
2. install requirements
3. register to Neptune (if you wish to use it)
4. run experiment:

$ neptune login
$ neptune experiment send --config neptune.yaml --worker gcp-large --environment base-cpu-py3 main.py train_evaluate_predict --pipeline_name solution_1

collect submit from /output/solution-1 directory.

1. clone this repository: git clone https://github.com/neptune-ml/open-solution-talking-data.git
2. install PyTorch and torchvision
3. install requirements: pip3 install -r requirements.txt
4. register to Neptune (if you wish to use it)
5. open Neptune and create new project called: talking-data with project key: TDAT
6. run experiment:

$ neptune login
$ neptune experiment send --config neptune.yaml --worker gcp-large --environment base-cpu-py3 main.py train_evaluate_predict --pipeline_name solution_1

collect submit from /output/solution-1 directory.

There are several ways to seek help:

1. Kaggle discussion is our primary way of communication.
2. You can submit an issue directly in this repo.

1. Check CONTRIBUTING for more information.
2. Check issues and project to check if there is something you would like to contribute to.