Gradient Descent With Momentum | Visual Explanation | Deep Learning #11
In this video, you’ll learn how Momentum makes gradient descent faster and more stable by smoothing out the updates instead of reacting sharply to every new gradient. We’ll see how the moving average of past gradients helps reduce zig-zags, why the beta parameter controls how smooth the motion becomes, and how this simple idea lets optimization reach the minimum more efficiently. By the end, you’ll understand not just the formula, but the intuition behind why momentum works so well in deep learning. Links for Important videos ✅ :- EWMA:- • Exponentially Weighted Moving Average (EWM... Gradient descent :- • How Gradient Descent REALLY Works Activation Functions:- • What Are Activation Functions? Deep Learn... Vanishing/Exploding gradients:- • Vanishing AND Exploding Gradient Problem E... Data Normalization:- • Data Normalization | Why Scaling Your Data... 📚 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:- / bytequest
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