Probabilistic Deep Learning

4 Building loss functions with the likelihood approach

This chapter covers

• Using the maximum likelihood approach for estimating model parameters
• Determining a loss function for classification problems
• Determining a loss function for regression problems

In the last chapter, you saw how you can determine parameter values through optimizing a loss function using stochastic gradient descent (SGD). This approach also works for DL models that have millions of parameters. But how did we arrive at the loss function? In the linear regression problem (see sections 1.4 and 3.1), we used the mean squared error (MSE) as a loss function. We don’t claim ...