There are various loss functions to pick from, and it can be difficult to know which one to use, or even what a loss function in deep learning is and what role it plays in neural network training.

In this post, you will learn about the role of loss and loss functions in deep learning neural network training, as well as how to select the best loss function in deep learning for your predictive modelling applications.

To calculate the model error, neural networks are trained using an optimization procedure that includes a loss function.

When training neural networks and machine learning models in general, Maximum Likelihood provides a framework for selecting a loss function.

When training neural network models, the two primary types of loss functions to use are cross-entropy and mean squared error.

Better Deep Learning, my latest book, includes step-by-step explanations and the Python source code files for all examples.

This tutorial is divided into seven sections, which are as follows:

Optimization Using Neural Network Learning

What Is the Difference Between aloss function in deep learning and a Loss?

Maximum Probability

Maximal Likelihood and Cross-Entropy

What loss function in deep learning Should I Use?

How to Use Loss Functions

Reported Model Performance and Loss Functions

We shall concentrate on the theory of loss functions.

See the following post for assistance in selecting and implementing various loss functions:

Optimization Using Neural Network Learning

From training data, a deep learning neural network learns to map a set of inputs to a set of outputs.

There are too many unknowns to compute the ideal weights for a neural network. Instead, the learning problem is framed as a search or optimization problem, and an algorithm is used to navigate the space of different weight settings that the model may employ in order to make good or adequate predictions.

A neural network model is typically trained with the stochastic gradient descent optimization process, and weights are updated with the backpropagation of error algorithm.

The term "gradient descent" refers to an erroneous gradient. The model is used to produce predictions using a given set of weights, and the error for those predictions is determined.

The gradient descent approach attempts to alter the weights so that the next assessment reduces the error, implying that the optimization algorithm is navigating down the error gradient (or slope).

We can now look at how the error of a particular set of weights is calculated now that we know that training neural networks solves an optimization issue.

What Is the Difference Between a loss function in deep learning and a Loss?

The objective function is the function used to evaluate a potential solution (i.e. a set of weights) in the context of an optimization process.

We may strive to maximise or decrease the objective function, which means we are looking for a candidate solution with the highest or lowest score.

We typically use neural networks to decrease error. As a result, the objective function is also known as a cost function or a loss function, and the value computed by the loss function in deep learning ssimply referred to as "loss."

The objective function or criterion is the function we wish to minimise or maximise. When we minimise it, we might refer to it as the cost function, loss function, or error function.

The cost or loss function in deep learning performs a vital purpose in that it must faithfully distil all characteristics of the model down to a single number in such a way that increases in that number indicate a better model.

The cost function lowers all of the positive and negative elements of a potentially complicated system to a single number, a scalar value, allowing candidate solutions to be rated and compared.

— Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, 1999, page 155.

A loss function in deep learning must be chosen to calculate the model's error during the optimization phase.

This can be a difficult challenge because the function must encapsulate the properties of the problem and be motivated by project and stakeholder concerns.

As a result, it is critical that the function accurately represents our design aims. If we use a poor error function and get unsatisfactory results, it is our fault for not properly expressing the search goal.

— Page 155, Supervised Learning in Feedforward Artificial Neural Networks, Neural Smithing, 1999.

We need to know which functions to utilise now that we are familiar with the loss function and loss.

Whatloss function in deep learning Should I Use?

We can summarise the previous part and immediately recommend the loss functions that you should employ within a maximum likelihood framework.

Importantly, the loss function you choose is intimately tied to the activation function you choose in your neural network's output layer. These two design aspects are linked together.

Consider the output layer configuration to be a choice concerning the framing of your prediction problem, and the loss function selection to be the method for calculating the error for a given framing of your problem.