I just started AI for diagnosis course. I was just wandering what exactly does loss mean?
and welcome to the community!
Loss refers to the „ error“ or „deviation“ between the prediction of a model and the ground truth (label).
When fitting your ML model (training) you want to minimize your loss so that the model can predict well the labels (e.g. predict that there is a cat on a picture when there is really a cat visible).
Feel free to check out this thread: Difference between loss function and cost function? - #2 by Christian_Simonis
Please let me know if anything is unclear.
Best
Christian
In machine learning, “loss” refers to a mathematical measure that quantifies the difference between the predicted output of a model and the actual ground truth. It represents how well or poorly the model is performing on a given task. The goal is to minimize this loss function, as a lower loss indicates a better fit of the model to the data.
To dive deeper into this concept and gain a better understanding, I found an informative article Loss Functions and Their Use In Neural Networks | by Vishal Yathish | Towards Data Science that explains you loss function in simple way. It provides insights into their mathematical formulations, applications, and how they impact the training process.
Feel free to ask any question regarding this or any other concept.
Regards,
Harsh Der