The output layer – Deep Learning: Getting Started Video Tutorial | LinkedIn Learning, formerly Lynda.com

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– The output layer is the final layer in the neural network where desired predictions are obtained. There is one output layer in a neural network that produces the desired final prediction. It has its own set of weights and biases that are applied before the final output is derived. The activation function for the output layer may be different than the hidden layers based on the problem. For example, Softmax activation is used to derive the final classes in a classification problem. The output is a vector of values that may need further post-processing to convert them to business related values. For example, in a classification problem, the output is a set of probabilities that needs to be mapped to the corresponding business classes. How do we determine the number of notes in the output layer? It depends on the problem. In a binary classification problem, there is only 1 note that provides a probability of the positive outcome. In the case of n-class classification, there are n notes, each producing the probability for a given class. For regression, there is only 1 note that produces the output. In this way, the number of notes may vary based on the type of problem being solved. This completes our discussion on the structure of the neural network. In the next chapter, we will explore more about training the network.