Optimizer

 

MACHINE lEARNING WITH PYTORCH, WHAT IS THE ROLE OF AN OPTIMIZER

In machine learning with PyTorch, an optimizer is responsible for updating the parameters of a model to minimize the loss function. The loss function is a measure of how well the model is performing on the training data. The optimizer updates the parameters in the direction that reduces the loss function the most.

There are many different optimizers available in PyTorch, each with its own strengths and weaknesses. Some popular optimizers include:

• Stochastic gradient descent (SGD): SGD is a simple and effective optimizer that updates the parameters in the direction of the negative gradient of the loss function.
• Adam: Adam is a more sophisticated optimizer that uses adaptive learning rates to update the parameters. This can help to improve the performance of the model and reduce the time it takes to train.
• RMSprop: RMSprop is another adaptive learning rate optimizer that is similar to Adam. It is often used for training recurrent neural networks.

The choice of optimizer depends on the specific model and dataset being used. It is often necessary to experiment with different optimizers to find the one that works best.

source: Bard

source: Chatgpt