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:- 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

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