n PyTorch, how do you apply a different learning rate to different layers of a model?By using the set_lr() method of the optimizer for each layerBy setting the lr attribute of the parameters of each layerBy passing dict mapping layers to learning rates to the optimizerBy passing a list of dicts to the optimizer, where each dict contains the `params` and `lr` for each group of parameters

Which optimization algorithm adapts the learning rate for each parameter based on its gradient history?

In PyTorch, how do you save only the parameters of a model, not the entire model?By using torch.save(model.weights(), PATH)By using torch.save(model.parameters(), PATH)By using torch.save(model.state_dict(), PATH)By using torch.save(model, PATH)

Which optimization algorithm adapts the learning rate for each parameter based on its gradient history?Group of answer choicesGradient DescentStochastic Gradient Descent (SGD)AdaGradRMSprop

What is the purpose of the learning rate in neural network optimization?Review LaterTo control the number of epochsTo determine the batch sizeTo adjust the step size during weight updatesTo set the initial weights

What is the learning rate in backpropagation?Select one:a.The process of adjusting the weights and biases in the backward directionb.The process of minimizing the error between the predicted output and the actual outputc.The process of adjusting the weights and biases in the forward directiond.The hyperparameter that determines the size of the weight and bias updates