torch in action

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

  • A thorough introduction to the why and how of image processing with deep learning is found in our book, Deep Learning and Scientific Computing with R torch.

  • 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

  • An interesting use case that illustrates the importance of domain knowledge is discussed the torch book.

  • 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.

  • torch, tidymodels, and high-energy physics introduces tabnet, a torch implementation of “TabNet: Attentive Interpretable Tabular Learning” that is fully integrated with the tidymodels framework. Thanks to tidymodels integration, both pre-processing and hyperparameter tuning need a minimal amount of code.

Time series forecasting

Audio processing

  • In its chapter on audio classification, the torch book shows, by example, the usefulness of integrating Fourier-domain representations with deep learning.