Regularization | L1 & L2 | Dropout | Data Augmentation | Early Stopping | Deep Learning Part 4

ByteQuest · Beginner ·📐 ML Fundamentals ·9mo ago

About this lesson

In this video, we dive into Regularization — the set of methods we use to deal with overfitting while training a Machine Learning Model including a deep neural network. We’ll start with L1 and L2 Regularization and then will move on the DropOut Regularization and then will move on to Data Augmentation and Early Stopping. By the end, you’ll have a clear intuition of how Regularization helps prevent overfitting. Timestamps:- 0:00 Why Use Regularization? 2:30 L1 and L2 7:01 DropOut Regularization 11:43 Data Augmentation 13:12 Early Stopping Links of related videos:- Overfitting & Underfitting:- https://youtu.be/C-Cd2ZGXBH4 Neural Networks:- https://youtu.be/sE6OaMndGZg Gradient Descent :- https://youtu.be/2xdUsy3oq-4 If you're passionate about learning complex concepts in the simplest way possible, you're in the right place. I create visual explanations using animations to make topics more intuitive and engaging—especially in Algorithms, AI, machine learning, and beyond. 🎥 Animations created using Manim: Manim is an open-source Python library for creating mathematical animations. Learn more or try it yourself: 🔗 https://www.manim.community Let's Connect:- GitHub:- https://github.com/ByteQuest0 Reddit:- https://www.reddit.com/r/ByteQuest/

Original Description

In this video, we dive into Regularization — the set of methods we use to deal with overfitting while training a Machine Learning Model including a deep neural network. We’ll start with L1 and L2 Regularization and then will move on the DropOut Regularization and then will move on to Data Augmentation and Early Stopping. By the end, you’ll have a clear intuition of how Regularization helps prevent overfitting. Timestamps:- 0:00 Why Use Regularization? 2:30 L1 and L2 7:01 DropOut Regularization 11:43 Data Augmentation 13:12 Early Stopping Links of related videos:- Overfitting & Underfitting:- https://youtu.be/C-Cd2ZGXBH4 Neural Networks:- https://youtu.be/sE6OaMndGZg Gradient Descent :- https://youtu.be/2xdUsy3oq-4 If you're passionate about learning complex concepts in the simplest way possible, you're in the right place. I create visual explanations using animations to make topics more intuitive and engaging—especially in Algorithms, AI, machine learning, and beyond. 🎥 Animations created using Manim: Manim is an open-source Python library for creating mathematical animations. Learn more or try it yourself: 🔗 https://www.manim.community Let's Connect:- GitHub:- https://github.com/ByteQuest0 Reddit:- https://www.reddit.com/r/ByteQuest/

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Chapters (5)

Why Use Regularization?

2:30 L1 and L2

7:01 DropOut Regularization

11:43 Data Augmentation

13:12 Early Stopping

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