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 stochastic average gradient?

We propose the stochastic average gradient (SAG) method for optimizing the sum of a finite number of smooth convex functions. Like stochastic gradient (SG) methods, the SAG method’s iteration cost is independent of the number of terms in the sum.

What is true for Stochastic Gradient Descent?

Stochastic Gradient Descent is a stochastic, as in probabilistic, spin on Gradient Descent. It improves on the limitations of Gradient Descent and performs much better in large-scale datasets. That’s why it is widely used as the optimization algorithm in large-scale, online machine learning methods like Deep Learning.

Which is faster SGD or Gd?

SGD is stochastic in nature i.e it picks up a “random” instance of training data at each step and then computes the gradient making it much faster as there is much fewer data to manipulate at a single time, unlike Batch GD.

Does SGD or Gd provide a more accurate estimate of the gradient?

SGD is much faster but the convergence path of SGD is noisier than that of original gradient descent. This is because in each step it is not calculating the actual gradient but an approximation. This is a process that uses the flexibility of SGD and the accuracy of GD.

Why do we prefer stochastic gradient descent?

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.