Optimizers are essential in training machine learning models, as they determine how the model updates its parameters to minimize the loss function. Here's a quick guide to popular optimization algorithms:
1. Stochastic Gradient Descent (SGD)
- Symbol: ⚡
- Description: A foundational optimizer that adjusts weights using the gradient of the loss function.
- Use Case: Suitable for simple models or when computational resources are limited.
- SGD
- Learn more about SGD
2. Adam Optimizer
- Symbol: 🧩
- Description: Combines the benefits of RMSProp and SGD with momentum, adapting learning rates dynamically.
- Use Case: Widely used in deep learning due to its efficiency and performance.
- Adam
- Explore advanced optimization techniques
3. RMSProp
- Symbol: 🔄
- Description: Focuses on adapting the learning rate based on the moving average of squared gradients.
- Use Case: Effective for non-stationary objectives and recurrent neural networks (RNNs).
- RMSProp
4. L-BFGS
- Symbol: 📈
- Description: A quasi-Newton method that approximates second-order derivatives for faster convergence.
- Use Case: Ideal for small to medium-sized datasets with smooth loss landscapes.
- L-BFGS
For deeper insights, check our optimizer comparison guide. 📘