What are the differences between Adam and SGD optimizers?

What are the differences between Adam and SGD optimizers?

Adam (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) are both optimization algorithms used in training machine learning models. Adam adapts the learning rate for each parameter based on the first and second moments of the gradients, while SGD uses a fixed learning rate and updates parameters based on the average of the gradients.

Which one is better for deep learning?

Adam is often preferred for deep learning tasks because it converges faster and is more robust to noisy data. However, SGD can perform better in some cases, especially with proper tuning of the learning rate and momentum.

Can you give an example of when to use SGD?

SGD is particularly useful in scenarios where you have a large dataset and want to avoid overfitting. It's also effective in fine-tuning models after initial training with Adam.

What are the hyperparameters for Adam?

Adam has several hyperparameters, including the learning rate, beta1 (the exponential decay rate for the first moment), beta2 (for the second moment), and epsilon (a small constant to prevent division by zero). Typical values are 0.001 for the learning rate, 0.9 for beta1, 0.999 for beta2, and 1e-7 for epsilon.