Career Essentials in Generative AI by Microsoft and LinkedIn Practice Test

In a neural network, the output layer is where the final predictions or classifications are made based on the processing performed by the previous layers. This layer takes the information from the hidden layers, which have transformed the input data, and produces a score that represents the likelihood of each class identified by the model. In the context of customer returns categorized as defective or unsatisfied, the output layer would generate the probability scores that indicate how likely it is that a given return falls into each of those categories.

The other layers have distinct roles: the input layer is responsible for receiving the raw input data, while the hidden layers perform intermediate computations and transformations but do not provide the final classification outcomes. The activation layer, typically applied within the hidden layers, helps introduce non-linearity into the model by applying activation functions, but it does not directly present the final probability scores. Therefore, the output layer is clearly where Kira will find the probability scores for the specified categories.