Optimizers For Deep Learning: A Guide To SGD, Momentum, And More
Explained: Batch, Mini-Batch & Stochastic Gradient Descent in PyTorch. Optimizers like SGD(), RMSprop() and Adam() accelerate convergence.
Buy Me a Coffee☕ *Memos: My post explains Batch, Mini-Batch and Stochastic Gradient Descent in PyTorch. My post explains SGD(). My post explains RMSprop(). My post explains Adam(). My post explains layers in PyTorch. My post explains activation functions in PyTorch. My post explains loss functions in PyTorch. An optimizer is the gradient descent algorism which can find the minimum(or maximum) gradient(slope) of a function by updating(adjusting) a model's parameters(weight and bias) to minimize the mean(average) of the sum of the losses(differences) between the model's predictions and train...