Hidden layer
In this article we are going to talk about Hidden layer, a topic that has gained relevance in recent years and that has generated a great debate in today's society. Hidden layer is a point of interest for many people, as it has a direct impact on different aspects of our daily lives. Throughout the next few lines we will explore this topic in depth, analyzing its implications, its evolution over time, and its relevance in various areas. Without a doubt, Hidden layer is a topic that does not leave anyone indifferent, and it is essential to understand it thoroughly to better understand the world around us.
In artificial neural networks, a hidden layer is a layer of artificial neurons that is neither an input layer nor an output layer. The simplest examples appear in multilayer perceptrons (MLP), as illustrated in the diagram.[1]
An MLP without any hidden layer is essentially just a linear model. With hidden layers and activation functions, however, nonlinearity is introduced into the model.[1]
In typical machine learning practice, the weights and biases are initialized, then iteratively updated during training via backpropagation.[1]
References
- ^ a b c Zhang, Aston; Lipton, Zachary; Li, Mu; Smola, Alexander J. (2024). "5.1. Multilayer Perceptrons". Dive into deep learning. Cambridge New York Port Melbourne New Delhi Singapore: Cambridge University Press. ISBN 978-1-009-38943-3.
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