The torch.optim package contains a number of optimization algorithms, and each of these algorithms has several parameters that we can use to fine-tune deep learning models. Optimization is a critical component in deep learning, so it is no surprise that different optimization techniques can be key to a model's performance. Remember, its role is to store and update the parameter state based on the calculated gradients of the loss function.
There are a number of optimization algorithms besides SGD available in PyTorch. The following code shows one such algorithm:
optim.Adadelta(params, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0)
The Adedelta algorithm is based on stochastic gradient...