Linear Regression (PyTorch) Exercise

Programming for Data Science is a subject we’ve designed to explore the various programming components of data science.

Keywords

data science, data analysis, programming, dataidea

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Week 3: Linear Regression in PyTorch (4 Questions)

1. Stochastic Gradient Descent

• Exercise: Implement a linear regression model using stochastic gradient descent (SGD) on a synthetic dataset. Plot the loss curve to show convergence over iterations.

2. Mini-Batch Gradient Descent

• Exercise: Modify your SGD implementation to use mini-batch gradient descent. Train the model on a dataset with mini-batches and compare the performance with the full-batch SGD approach.

3. PyTorch Built-in Functions

• Exercise: Use PyTorch’s built-in functions (torch.nn.Linear, torch.optim.SGD) to build and train a linear regression model. Compare the results with your previous implementations.

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4. Training and Validation Sets

• Exercise: Split your dataset into training and validation sets. Train a linear regression model on the training set and evaluate its performance on the validation set. Plot the training and validation loss over epochs.

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