SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 2
Channel: CampusX
Date: 2022-07-20

In this video, we will understand in detail what is Momentum Optimizer in Deep Learning. Momentum Optimizer in Deep Learning is a technique that reduces the time taken to train a model.  The path of learning in mini-batch gradient descent is zig-zag, and not straight. Thus, some time gets wasted in moving in a zig-zag direction. Momentum Optimizer in Deep Learning smooths out the zig-zag path and makes it much straighter, thus reducing the time taken to train the model.

Momentum Optimizer uses Exponentially Weighted Moving Average, which averages out the vertical movement and the net movement is mostly in the horizontal direction. Thus zig-zag path becomes straighter.

Digital Notes for Deep Learning: https://shorturl.at/NGtXg

Convex Vs Non-convex Cost Function:
https://www.youtube.com/watch?v=TXVtbgaEyms&t=1s&ab_channel=CampusX

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⌚Time Stamps⌚

00:00 - Intro
01:24 - Understanding Graphs
07:13 - Image Representation
10:41 - Convex vs Non-Convex Optimization
18:13 - Momentum Optimization
20:49 - The What?
24:23 - How to implement the concept Mathematically
28:41 - Effect of Beta
34:33 - Problems with Momentum Optimization
35:54 - Visualization
38:04 - Outro