Training a Single Output Multilinear Regression Model in PyTorch
A neural network architecture is built with hundreds of neurons where each of them takes in multiple inputs to perform a multilinear regression operation for prediction. In the previous tutorials, we built a single output multilinear regression model that used only a forward function for prediction.
In this tutorial, we’ll add optimizer to our single output multilinear regression model and perform backpropagation to reduce the loss of the model. Particularly, we’ll demonstrate:
- How to build a single output multilinear regression model in PyTorch.
- How PyTorch built-in packages can be used to create complicated models.
- How to train a single output multilinear regression model with mini-batch gradient descent in PyTorch.
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