Introduction to PyTorch Optimizers: Practical Implementation of Optimization Algorithms like SGD and Adam

### Optimizers: The "Navigation System" for Deep Learning Optimizers are core tools in deep learning for updating model parameters and minimizing loss functions, similar to a navigation system when climbing a mountain, guiding the model from "high-loss" peaks to "low-loss" valleys. Their core task is to adjust parameters to improve the model's performance on training data. Different optimizers are designed for distinct scenarios: The basic SGD (Stochastic Gradient Descent) is simple but converges slowly and requires manual hyperparameter tuning; SGD+Momentum incorporates "inertia" to accelerate convergence; Adam combines momentum and adaptive learning rates, performing exceptionally well with default parameters and being the first choice for most tasks; AdamW adds weight decay (L2 regularization) to Adam, effectively preventing overfitting. PyTorch's `torch.optim` module provides various optimizers: SGD is suitable for simple models, SGD+Momentum accelerates models with fluctuations (e.g., RNNs), Adam adapts to most tasks (e.g., CNNs, Transformers), and AdamW is ideal for small datasets or complex models. In practical tasks, comparing linear regression (e.g., `y=2x+3`), Adam converges faster with smoother loss and parameters closer to the true values, while SGD is prone to oscillations. Beginners are advised to prioritize Adam, and if parameter control is required... (Note: The original text cuts off here, so the translation concludes at the available content.)