Meet the stemany family

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y (Output)

Stemany Notebook

Creating Data. Using PyTorch

Splitting Data

Data splitting is a crucial step in the machine learning workflow. It involves dividing the dataset into separate parts to train and evaluate the performance of a model. Proper splitting helps ensure that the model generalises well to new, unseen data

Training Data
Validating Data (Optional)
Testing Data

Building Model

To build a linear regression model in PyTorch, define a custom model class inheriting from nn.Module. Initialise it with a single linear layer using nn.Linear, which handles one input and one output feature. The forward method processes the input through this layer. Instantiate the model, set a manual seed for reproducibility, and use state_dict to access the model's parameters for saving and loading.

Author. Manish Yadav

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