Difference between two Optimizers

 

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SGD and Adam

SGD (Stochastic Gradient Descent) and Adam (Adaptive Moment Estimation) are both optimization algorithms commonly used in machine learning to update the parameters of a model during training. However, there are some key differences between them:

1. Update Rule: SGD updates the model parameters using the gradient of the loss function with respect to the parameters. The update rule is: param = param - learning_rate * gradient

Adam, on the other hand, uses a combination of the gradient and the moving average of the past gradients. The update rule is: param = param - learning_rate * (m_t / (sqrt(v_t) + eps)) where m_t is the first moment (mean) of the gradient, v_t is the second moment (variance) of the gradient, eps is a small constant to avoid division by zero.

1. Learning Rate: SGD uses a fixed learning rate, which can be set manually or adaptively tuned during training.

Adam, however, adapts the learning rate for each parameter based on the historical values of the gradients. This allows it to dynamically adjust the learning rate for each parameter, which can lead to faster convergence and better generalization.

1. Momentum: SGD does not use momentum, which means that the updates can oscillate and take longer to converge.

Adam incorporates a momentum term, which helps to smooth out the updates and accelerates convergence. The momentum term is similar to the moving average of the gradients used in the update rule.

1. Performance: In practice, Adam is generally considered to be a more robust optimizer than SGD. It often requires fewer iterations to converge and can handle noisy or sparse gradients more effectively.

However, in some cases, SGD can outperform Adam, especially when the data is clean and the learning rate is carefully tuned. Additionally, SGD is often faster than Adam when the data set is small or the model is simple.

Overall, the choice between SGD and Adam (or any other optimizer) depends on the specific problem, the size of the data set, the complexity of the model, and the computational resources available.