What is the difference between stochastic gradient descent & standard gradient descent?

The only difference comes while iterating. In Gradient Descent, we consider all the points in calculating loss and derivative, while in Stochastic gradient descent, we use single point in loss function and its derivative randomly.

What is Alternating gradient descent?

Alternating gradient descent (A-GD) is a simple but popular algorithm in machine learning, which updates two blocks of variables in an alternating manner using gradient descent steps.

What is the Stochastic Gradient Descent Why do we need Stochastic Gradient Descent?

Gradient Descent is the most common optimization algorithm and the foundation of how we train an ML model. But it can be really slow for large datasets. That’s why we use a variant of this algorithm known as Stochastic Gradient Descent to make our model learn a lot faster.

How does Stochastic Gradient Descent work?

SGD randomly picks one data point from the whole data set at each iteration to reduce the computations enormously. It is also common to sample a small number of data points instead of just one point at each step and that is called “mini-batch” gradient descent.

What is alternating optimization algorithm?

Alternating optimization (AO) is an iterative procedure for minimizing f(x) = f(X1, X2,…, Xt) jointly over all variables by alternating restricted minimizations over the individual subsets of variables X1,…., Xt. Alternating optimization has been (more or less) studied and used in a wide variety of areas.

How do you use stochastic gradient descent?

How to move down in steps?

1. Find the slope of the objective function with respect to each parameter/feature.
2. Pick a random initial value for the parameters.
3. Update the gradient function by plugging in the parameter values.
4. Calculate the step sizes for each feature as : step size = gradient * learning rate.

What is the difference between the gradient descent and stochastic gradient descent methods which one is preferred when?

Generally stochastic GD is preferred for being faster as it is optimizing parameter on one training example at a time till it converges. On the other hand, gradient descent(called Batch GD) optimizes parameter on whole training set every iteration till convergence. This makes Batch GD slow but deterministic.