Which of the following is a commonly used activation function in deep learning?

In deep learning, activation functions play a crucial role in introducing non-linearity into the model, allowing it to learn complex patterns in the data. The choice of activation function can significantly impact the performance of a neural network.

The linear activation function outputs the input directly, which may not be suitable for layers where complex relationships need to be modeled. However, in specific contexts, like the output layer of a regression model, it can still be useful.

The sigmoid function, which maps any real-valued number into a value between 0 and 1, is particularly common in binary classification tasks. It transforms the inputs into probabilities, making it effective for models where binary outcomes are expected.

The softmax activation function is often used in models designed for multi-class classification problems. It takes a vector of raw prediction scores and converts them into probabilities that sum to 1, enabling straightforward interpretation of the predictions in terms of class probabilities.

By including all of these functions as options, the correct answer acknowledges that each activation function can be appropriate depending on the context of the deep learning problem being solved. Therefore, "All of the above" is a comprehensive choice, recognizing the versatility and applicability of different activation functions across various layers and tasks in neural networks.