101ai.net | Lesson 4 | Basics | Gradient Descent

In this video, we explore the crucial concept of Gradient Descent in machine learning. Gradient Descent is essential for minimizing the error in our model's predictions by iteratively adjusting model parameters. This lesson provides a hands-on demonstration using a specialized tool to help you visualize how Gradient Descent works. We cover the steps involved in calculating gradients, updating parameters, and observing the loss function's behavior over time.

Key Concepts Covered:
Gradient Descent
Learning Rate
Loss Function

By the end of this video, you'll understand the importance of Gradient Descent and how to apply these techniques to optimize your AI and machine learning models.

Try it Yourself:
Use our interactive tool to experiment with provided datasets or input your own data to see how Gradient Descent works in practice.

Notes and Tutorials:

Chapters:
0:02 Introduction
0:13 What is Gradient Descent
2:20 Initialize the algorithm
3:58 Graphs and charts
5:14 Convergence
6:40 Conclusion

Important Note:
The content of this video is for educational purposes only. While every effort has been made to ensure accuracy, we are not responsible for any errors or omissions. Always verify and consult multiple sources when applying these techniques in real-world applications.

Fair Use Disclaimer:
This video is for educational purposes only. All content, including images and formulas, are used under fair use for teaching and scholarship. We do not claim any ownership of external resources used for illustrative purposes.

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