Stochastic Gradient Descent Algorithm With Python and NumPy

By realpython - 2021-02-02

Description

In this tutorial, you'll learn what the stochastic gradient descent algorithm is, how it works, and how to implement it with Python and NumPy.

Summary

Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
The learning rate determines how large the update or moving step is.
A lower learning rate prevents the vector from making large jumps, and in this case, the vector remains closer to the global optimum.
The best regression line is 𝑓(𝑥) = 5.63 + 0.54𝑥.
As in the previous examples, this result heavily depends on the learning rate.

Topics

Machine_Learning (0.54)
Backend (0.21)
NLP (0.12)

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