Visualizing Gradient Descent Parameters in Torch

This post explores the effects of different parameters on model training using Torch's stochastic gradient descent optimizer. It includes a toy problem of performing linear regression, visualizations of loss functions, and the impact of parameters like momentum, weight decay, dampening, and nesterov momentum.