The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function.
In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. This is how a Neural Net is trained.
Keras and Tensorflow have various inbuilt loss functions for different objectives. Following essential loss functions are available which could be used for most of the objectives.
• Mean Squared Error (MSE)
• Binary Crossentropy (BCE)
• Categorical Crossentropy (CC)
• Sparse Categorical Crossentropy (SCC)
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