Think Different

The full form of SGD is Stochastic Gradient Descent . It is an iterative optimization algorithm that is used to find the minimum of a function. SGD works by randomly selecting one data point at a time and updating the parameters of the model in the direction of the negative gradient of the function at that data point. SGD is a popular algorithm for training machine learning models, especially neural networks. It is relatively simple to implement and can be used to train models on large datasets. However, SGD can be slow to converge and may not always find the global minimum of the function.  I can explain how SGD works with an example. Let's say we have a neural network that is trying to learn to predict the price of a stock. The neural network has a set of parameters, such as the weights and biases of the individual neurons. The goal of SGD is to find the values of these parameters that minimize the error between the predicted prices and the actual prices. SGD works by iteratively...