Loss Function

A function that computes how far-off a model’s output was from the expected output during training. Takes in the:

• Weights
• Biases
• Desired output If often a very high dimensional function. Gradient Descent is the process to revise based off a loss function.

Implementations

• Mean Absolute Error
• Mean Squared Error
• Root Mean Squared Error
• Huber Loss

Problems

• Cost Functions Are Too Confident