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tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, and imputation. Starting with tsai 0.3.0 tsai will only install hard dependencies. Other soft dependencies (which are only required for selected tasks) will not be installed by default (this is the recommended approach. If you require any of the dependencies that is not installed, tsai will ask you to install it when necessary) We've also added a new PredictionDynamics callback that will display the predictions during training. This is the type of output you would get in a classification task. New tutorial notebook on how to train your model with larger-than-memory datasets in less time achieving up to 100% GPU usage! See our new tutorial notebook on how to track your experiments with Weights & Biases
Features
- Starting with tsai 0.3.0 you'll get faster installs and imports through a better use of dependencies
- New visualization methods
- learn.feature_importance() and learn.step_importance() will help you gain better insights on how your models works
- New calibration model
- tsai is currently under active development by timeseriesAI
- Provides tutorials
Project Samples
