You’ve run your first torch demo, and acquainted yourself with the main actors (tensors, modules, optimizers)? Then you’re ready to dive into applied examples. The list of examples keeps growing as the ecosystem evolves. What area are you interested in?
Image recognition
Bird classification is a multi-class classification task. In addition to being a blueprint for doing classification with torch, this introductory example shows how to load data, make use of pre-trained models, and benefit from learning rate schedulers.
Brain image segmentation builds a U-Net from scratch. This intermediate-level example is a great introduction to building your own modules, as well as custom datasets that perform data preprocessing and data augmentation for computer vision.
Tabular data
- Labeling poisonous mushrooms is a first introduction to handling a mix of numerical and categorical data, using embedding modules for the latter. It also provides a blueprint for creating torch models from scratch.
Time series forecasting
- Convolutional LSTM for spatial forecasting is an intermediate-level example that shows how to build a convolutional LSTM from scratch.