RMSProp Optimizer Visually Explained | Deep Learning #12
About this lesson
In this video, you’ll learn how RMSProp makes gradient descent faster and more stable by adjusting the step size for every parameter instead of treating all gradients the same. We’ll see how the moving average of squared gradients helps control oscillations, why the beta parameter decides how quickly the optimizer reacts to changes, and how this simple trick allows the model to move smoothly toward the minimum. By the end, you’ll understand not just the equation, but the intuition behind why RMSProp is such a powerful optimization method in deep learning. Links for Important videos ✅ :- EWMA:- https://youtu.be/dlajqZn7bjM Gradient descent :- https://youtu.be/2xdUsy3oq-4 Activation Functions:- https://youtu.be/Kz7bAbhEoyQ Vanishing/Exploding gradients:- https://youtu.be/CzNFuL_5uig Data Normalization:- https://youtu.be/W2vqsTg-rDU 📚 Welcome to the Channel! 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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