L1 and L2 Regularization in Machine Learning: Easy Explanation for Data Science Interviews

Описание: Regularization is a machine learning technique that introduces a regularization term to the loss function of a model in order to improve the generalization of a model. In this video, I explain both L1 and L2 regularizations, the main differences between the two methods, and leave you with helpful pros and cons so you can best decide when to implement each function.


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Contents of this video:
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00:00 Introduction
00:21 Interview Questions
00:41 What is regularization?
01:27 When to use regularization?
01:47 Regularization techniques
03:44 L1 and L2 regularizations
03:55 L1 Regularization
08:03 L2 Regularization
10:50 L1 vs. L2 Regularization
11:47 Outro