Beginner-Friendly: Basics of PyTorch Loss Functions and Training Loops

This article introduces the roles and implementation of loss functions and training loops in machine learning. Loss functions measure the gap between model predictions and true labels, while training loops adjust parameters to minimize loss for model learning. Common loss functions include: Mean Squared Error (MSE) for regression tasks (e.g., housing price prediction), accessible via `nn.MSELoss()` in PyTorch, and Cross-Entropy Loss for classification tasks (e.g., cat-dog recognition), accessible via `nn.CrossEntropyLoss()`. The core four steps of a training loop are: forward propagation (model prediction) → loss calculation → backpropagation (gradient computation) → parameter update (optimizer adjustment). It is critical to zero out gradients before backpropagation. Using linear regression as an example, the article generates simulated data, defines a linear model, trains it with MSE loss and the Adam optimizer, and iteratively optimizes parameters. Key considerations include: gradient zeroing, switching between training/inference modes, optimizer selection (e.g., Adam), and batch training with DataLoader. Mastering these concepts enables models to learn patterns from data, laying the foundation for complex models.