This video is an overall package to understand L2 Regularization Neural Network and then implement it in Python from scratch.

L2 Regularization neural network it a technique to overcome overfitting.

When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set and struggle to make good predictions on test dataset.

Overfitting in Deep Learning can be the result of having a very deep neural network or high number of neurons. With L2 Regularization Neural Network, we nullifying the effect of certain neurons, and thus, we create a simple network that will generate decision boundary that fits well in both training as well as test dataset.

If our model is not overfitting, then we need not use L2 Regularization. But when our model is overfitting, only then we use L2 Regularization.



Download the ASSIGNMENT and Implementation Code : https://github.com/Coding-Lane/L2-Regularization



Neural Network in Python from Scratch : https://www.youtube.com/watch?v=vtx1iwmOx10&t=284s&pp=sAQA

Complete Neural Network playlist : https://www.youtube.com/watch?v=mlk0rddP3L4&list=PLuhqtP7jdD8CftMk831qdE8BlIteSaNzD



Overfitting and Underfitting :https://www.youtube.com/watch?v=SOI39DEHGSk&t=0s

Complete Logistic Regression Playlist : https://www.youtube.com/watch?v=U1omz0B9FTw&list=PLuhqtP7jdD8Chy7QIo5U0zzKP8-emLdny

Complete Linear Regression Playlist : https://www.youtube.com/watch?v=nwD5U2WxTdk&list=PLuhqtP7jdD8AFocJuxC6_Zz0HepAWL9cF



Timestamp:
0:00 Video Agenda
0:59 Overfitting in Deep Learning
1:47 Why Overfitting occurs
3:52 L2 Regularization
5:58 L1 Regularization
6:40 L2 Regularization in Neural Network
9:15 L2 Regularization in Python
14:58 Changing value of Lambda



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