Stochastic gradient descent is a machine learning algorithm that is used to minimize a cost function by iterating a weight update based on the gradients. If you’ve never used the SGD classification algorithm before, this article is for you. In this article, I’ll give you an introduction to the Stochastic Gradient Descent Algorithm in Machine Learning and its implementation using Python.
Stochastic Gradient Descent
The SGD algorithm is used in several loss functions. Simply put, it is used to minimize a cost function by iterating a gradient-based weight update. Instead of looking at the full dataset, the weight update is applied to batches randomly extracted from it, which is why it is also known as mini-batch gradient descent.
Below is the process of the stochastic gradient descent algorithm:
- The algorithm starts at a random point by initializing the weights with random values
- Then it calculates the gradients at that random point
- Then it moves in the opposite direction of the gradient
- The process continues to repeat itself until it finds the point of minimum loss
Stochastic Gradient Descent using Python
Hope you now understand what the SGD algorithm in machine learning is. Now let’s see its implementation using Python:
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Summary
So this is how you can implement the SGD classification algorithm in machine learning by using the Python programming language. This algorithm is used in several loss functions. Simply put, it is used to minimize a cost function by iterating a gradient-based weight update. I hope you liked this article on the Stochastic Gradient Descent algorithm in Machine Learning and its implementation using Python. Feel free to ask your valuable questions in the comments section below.
