Respuesta :
A popular and frequently employed technique, stochastic gradient descent is the cornerstone of neural networks and other machine learning methods. I've done my best to describe it in this essay in both detail and layman's terms. Before continuing with this tutorial, I strongly advise running a linear regression.
What is Stochastic gradient:
Gradient simply refers to a surface's slope or tilt. In order to get to the lowest point on the surface, one must literally descend a slope.
I demonstrate the gradient descent approach using a linear regression problem. As we recall from this essay, the goal of regression is to reduce the sum of squared residuals. When the slope equals 0, we know that a function has reached its minimal value. This method allowed us to learn the weight vector and solve the linear regression problem. The gradient descent method can be used to solve the same issue.
For each iteration of Stochastic Gradient Descent, a small number of samples are chosen at random rather than the entire data set. The number of samples from a dataset that are utilized to calculate the gradient for each iteration is referred to as the "batch" in the Gradient Descent algorithm.
The optimization procedure stochastic gradient descent is frequently used in machine learning applications to identify the model parameters that best match the expected and actual outputs. It is a crude but effective method. The machine learning industry frequently uses stochastic gradient descent.
Learn more about Stochastic Gradient:
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