Machine Learning

Loss and Loss Functions for Training Deep Learning Neural Networks

Last Updated on October 23, 2019

Neural networks are trained using stochastic gradient descent and require that you choose a loss function when designing and configuring your model.

There are many loss functions to choose from and it can be challenging to know what to choose, or even what a loss function is and the role it plays when training a neural network.

In this post, you will discover the role of loss and loss functions in training deep learning neural networks and how to choose the right loss function for your predictive modeling problems.

After reading this post, you will know:

  • Neural networks are trained using an optimization process that requires a loss function to calculate the model error.
  • Maximum Likelihood provides a framework for choosing a loss function when training neural networks and machine learning models in general.
  • Cross-entropy and mean squared error are the two main types of loss functions to use when training neural network models.

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