Python - Gradient Descent: Step-by-Step to Find the Minimum!

Hey there! 👋 Welcome to the channel! 🌟

In this video, I’m showing you an easy way to grasp the idea of a gradient descent using Python. Whether you're new to this or need a refresher, this tutorial is for you!

Simple explanation of gradient descent:
Gradient Descent is a fundamental optimization algorithm used to minimize a function by iteratively adjusting its parameters. Imagine you're trying to find the lowest point in a hilly landscape, and you're standing on a hilltop. To get to the lowest point, you would take small steps in the direction that goes downhill the steepest.

In mathematical terms, the “gradient” refers to the slope of the function, which indicates the direction of steepest ascent. By moving in the opposite direction of the gradient, we can gradually approach the minimum value of the function. Anyway, with this video you would see that sometimes it is possible to miss the minimum value of the function, due to size of the step, number of steps taken or generally wrong understanding.

📊 GitHub repository with the code:
https://github.com/Vitosh/Python_pers...

📝Contents:
Introduction - 0:05
What is Gradient Descent? - 1:00
Step-by-Step Gradient Descent Process - 4:13
The Excel model - 7:30
Visualizing Gradient Descent - 9:50
What Happens When It Skips the Minimum? - 20:34
Choosing the Right Learning Rate - 25:05
Summary - 27:55

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🔧 Thanks for watching, and happy coding!
💡 #python #gradientdescent #vitoshacademy #machinelearning #programming 🎨