3.2. Object-Oriented Design for Implementation — Dive into Deep Learning 1.0.3 documentation

In our introduction to linear regression, we walked through various components including the data, the model, the loss function, and the optimization algorithm. Indeed, linear regression is one of the simplest machine learning models. Training it, however, uses many of the same components that other models in this book require. Therefore, before diving into the implementation details it is worth designing some of the APIs that we use throughout. Treating components in deep learning as objects, we can start by defining classes for these objects and their interactions. This object-oriented design for implementation will greatly streamline the presentation and you might even want to use it in your projects. Inspired by open-source libraries such as PyTorch Lightning, at a high level we wish to have three classes: (i) Module contains models, losses, and optimization methods; (ii) DataModule provides data loaders for training and validation; (iii) both classes are combined using the Trainer

3.2. Object-Oriented Design for Implementation — Dive into Deep Learning 1.0.3 documentation 3.2. Object-Oriented Design for Implementation ¶ Colab [pytorch] Open the notebook in Colab Colab [mxnet] Open the notebook in Colab Colab [jax] Open the notebook in Colab Colab [tensorflow] Open the notebook in Colab SageMaker Studio Lab Open the notebook in SageMaker Studio Lab In our introduction to linear regression, we walked through various components including the data, the model, the loss function, and the optimization algorithm. Indeed, linear regression is one of the simplest machine le

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