What is Loss and Loss function?

In the field of deep learning and Machine learning, the “_Loss” _is the forfeit of poor prediction. That means the Loss suggested how much satisfactory or awful prediction of the model is. If the Loss is high, that means the model is not giving a satisfactory result on test data, and if the Loss is less, it indicates the model is performing well on the test data. The procedure involved to calculate Loss is called loss function. For the calculation of Loss, various optimization techniques are used in the field of Machine learning and Deep learning. This article will cover commonly used loss function in Machine learning and Deep learning, its use and mathematics behind it. The most common losses used in Machine learning and Deep learning is:

1. Categorical Crossentropy
2. Binary Crossentropy
3. Mean Absolute Error
4. Mean Squared Error

#deep-learning #loss-function #machine-learning