# Loss Functions in Deep Learning

## A Guide on the Concept of Loss Functions in Deep Learning — What they are, Why we need them…

**This in-depth article addresses the questions of why we need loss functions in deep learning and which loss functions should be used for which tasks.**

**In Short:** *Loss functions in deep learning are used to measure how well a neural network model performs a certain task.*

**Table of Content**

- Why do we need Loss Functions in Deep Learning?
- Mean Squared Error Loss Function
- Cross-Entropy Loss Function
- Mean Absolute Percentage Error
- Take-Home-Message

## 1. Why do we need Loss Functions in Deep Learning?

Before we discuss different kinds of loss functions used in Deep Learning, it would be a good idea to address the question of why we need loss functions in the first place.

I think you must be familiar by now with the mathematical operations which are happening inside a neural network. Basically, there are just two:

- Forward Propagation
- Backpropagation with Gradient Descent