This project demonstrates a complete end-to-end PyTorch workflow for building, training, evaluating, and saving a simple linear regression model from scratch.
- Creating synthetic data using a known linear relationship
- Splitting data into training and testing sets
- Visualizing data and predictions with Matplotlib
- Building a custom model using
torch.nn.Module - Making predictions before and after training
- Implementing a full training & evaluation loop
- Using loss functions and optimizers (
L1Lossand SGD) - Saving and loading trained models
- Forward Pass: Computing predictions using model parameters
- Loss Calculation: Measuring how far predictions are from actual values
- Backpropagation: Updating weights using gradients
- Model Evaluation: Testing performance on unseen data
- Model Persistence: Saving and reloading trained models
The model learns to approximate a simple linear function:
y = 0.7x + 0.3
Starting from random parameters, the model gradually improves through training until it closely matches the true relationship.
This is a foundational project for understanding how deep learning works under the hood. It builds intuition for:
- How models learn
- How training loops operate
- How PyTorch handles gradients and optimization
- Beginners learning PyTorch
- Anyone transitioning from theory to practical ML
- Building a strong foundation before deep learning projects
💡 This project is a stepping stone to more advanced models like neural networks and real-world machine learning systems.