101ai.net | Lesson 14 | Deep Learning | Loss Functions

In this video, we explore the concept of loss functions in machine learning and their crucial role in model training. Loss functions quantify the difference between predicted and actual values, guiding model adjustments. We cover Mean Square Error (MSE), Mean Absolute Error (MAE), Huber Loss, Log Loss, and Hinge Loss. The video includes hands-on demonstrations using an interactive tool to visualize these functions, helping you understand their formulas and applications in machine learning models.

Try it Yourself

Notes and Tutorials:

Chapters
0:03 Introduction
0:35 Common Loss Functions
1:39 Mean Square Error (MSE)
2:46 Mean Absolute Error (MAE)
3:25 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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