Regularization: Make your Machine Learning Algorithms “Learn”, not “Memorize”

Regularization in the context of machine learning refers to a collection of strategies that help the machine learn more than solely memorize. In the simplest sense, ‘regularization’ indicates a set of techniques that regularizes learning from features for traditional algorithms or neurons in the case of neural network algorithms. Regularization is one of the major & most important concepts of machine learning. Regularization in machine learning prevents the model from overfitting. It basically reduces or regularizes the coefficient of features towards zero.

As mentioned above, regularization in machine learning refers to a set of techniques that help the machine to learn more than just memorize. Before we explore the concept of regularization in detail, let’s discuss what the terms ‘learning’ and ‘memorizing’ mean from the perspective of machine learning.

Know More@ https://www.einfochips.com/blog/regularization-make-your-machine-learning-algorithms-learn-not-memorize/#:~:text=Regularization%20in%20machine%20learning%20prevents,learn%20more%20than%20just%20memorize.

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