Deep neural networks (DNN) have been successfully used in diverse domains to solve real-world complex problems. The DL architectures use activation functions (AFs). Various activation functions have been proposed in the literature for both classification and regression tasks. This section surveys the activation functions that have been employed in the past as well as the current state of the art activation functions.
In this part of the series we begin our discussion of deep learning. We first talk about the human brain and the biological inspiration. Then we outline the history of the most elementary neural networks known as the perceptron. We discuss the architecture of the perceptron and its ability to function as a supervised linear classifier, using step function based activation functions.
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