In this video, we're going to unlock the mystery behind Gradient Descent, the algorithm that powers most machine-learning and deep-learning models. Ever wondered how machines get smarter overtime? Well, its all thanks to some fundamental math, and Gradient Descent is at the heart of it.
We'll break down this algorithm, covering some essential concepts along the way, including loss functions and regression models showing you how it helps machines improve their predictions by gradually minimizing errors. Think of it like finding the lowest point on a hilly landscape, but instead of hiking, you're using math to guide the way.
By the end of this video, you'll understand why Gradient Descent is essential to AI and how it helps systems "learn" from data. Whether you're just starting with machine learning or looking to strengthen your understanding, this video will give you the tools to tackle this crucial concept with confidence.
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0:00 Introduction
1:21 Plotting the dataset
2:00 Meaning of training a regression model
2:50 Regression model
3:19 What are model parameters?
3:34 How parameters affect the regression model
4:35 What is an error?
5:25 Deriving the MSE and MAE loss functions
6:36 What model training generally means
7:44 Gradient Descent
8:24 Data standardisation
8:50 Plotting MSE loss function
9:17 Training the model with one parameter
14:18 Effect of large learning rates
15:16 Training the model with two parameters
18:00 The 3D plot of MSE loss function
18:42 Experimenting with different datasets