Sorry I think I explained poorly. Plexe does build deep learning models automatically. When it gets a dataset and a problem description, it automatically evaluates various model architectures (NNs being one of them).

Plexe experiments with multiple approaches - from traditional algorithms like gradient boosting to deep neural networks. It runs the training jobs and compares performance metrics across different architectures to identify which solution best fits your specific data and problem constraints.

Oh okay! In that case, my faith is restored. Sounds like a cool project.

phew that was close. I'm glad your faith is restored :)

No, not by default. In fact, the default installation of plexe doesn't include deep learning libraries.

Plexe _can_ build deep learning models using `torch` and `transformers`, and often the experimentation process will include some NN-based solutions as well, but that's just one of the ML frameworks available to the agent. It can also build models using xgboost, scikit-learn, and several others.

You can also explicitly tell Plexe not to use neural nets, if that's a requirement.

Indeed your colleague explained similarly. Seems like a great project.