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

This post will teach you about the role of loss and loss functions in deep learning neural network training, as well as how to choose the optimum deep learning loss function in deep learning for your predictive modelling applications.

Neural networks are trained using an optimization technique that incorporates a loss function in deep learning to calculate model error.

Maximum Likelihood provides a framework for determining a loss function in deep learning for training neural networks and machine learning models in general.

Cross-entropy and mean squared error are the two most common loss functions to use while training neural network models.

Better Deep Learning, my most recent book, offers detailed explanations as well as the Python source code files for all examples.

This tutorial is divided into the following seven sections:

Neural Network Learning for Optimization

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

Maximum Likelihood

Cross-Entropy and Max Likelihood

What loss function should I use in deep learning?

How to Make Use of Loss Functions

Model Performance and Loss Functions as Reported

We will concentrate on loss function theory.

For help selecting and implementing various loss functions, see the following post:

Neural Network Learning for Optimization

There are too many unknowns to compute the optimal neural network weights. Instead, the learning problem is framed as a search or optimization problem, with an algorithm used to navigate the universe of alternative weight values that the model can utilise to create good or adequate predictions.

The stochastic gradient descent optimization technique is commonly used to train a neural network model, and the backpropagation of error procedure is used to update weights.

The phrase "gradient decline" refers to an incorrect gradient. The model is used to make predictions based on a set of weights, and the error for those predictions is calculated.

The gradient descent method aims to change the weights so that the next evaluation reduces the error, meaning that the optimization process is going down the error gradient (or slope).

Now that we know that training neural networks solves an optimization problem, we can investigate how the error of a certain set of weights is computed.

What Is the Difference Between a Deep Learning Loss Function and a Loss?

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

We might try to maximise or minimise the objective function, which means we're seeking for the best or worst potential solution.

To reduce error, we often employ neural networks. As a result, the objective function is also known as a cost function or a loss function, and the value generated by the loss function is simply referred to as "loss" in deep learning.

The function we want to minimise or maximise is known as the objective function or criteria. When we minimise it, we might call it the cost function, the loss function, or the error function.

The cost or loss function in deep learning serves an important function in that it must faithfully distil all model attributes down to a single number, with increases in that number indicating a stronger model.

The cost function reduces all positive and negative elements of a potentially complex system to a single number, known as a scalar value, allowing prospective solutions to be rated and compared.

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

During the optimization phase of deep learning, a loss function must be chosen to calculate the model's error.

This can be a difficult challenge because the function must embody the problem's attributes while being motivated by project and stakeholder concerns.

As a result, the function must appropriately represent our design goals. If we employ an ineffective error function and get unsatisfactory results, it is our fault for not defining the search goal clearly.

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

Now that we've learned about the loss function and loss, we need to know which functions to use.

What deep learning loss function should I use?

We may quickly summarise the previous section and offer the loss functions that you should use within a maximum likelihood framework.

Importantly, the loss function you select is inextricably linked to the activation function you select at the output layer of your neural network. These two design elements are related.

Consider the output layer configuration as a decision for the framing of your prediction problem, and the loss function selection as the mechanism for calculating the error for a specific framing of your problem.