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Multilayer Perceptrons (MLPs) in Deep Neural Network Architecture
Manage episode 444048752 series 3605861
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Let's explore multilayer perceptrons (MLPs), a type of deep neural network architecture. The text first discusses the limitations of linear models and how they struggle to capture complex non-linear relationships in data. It then introduces hidden layers as a solution, explaining how they allow MLPs to represent non-linear functions. The excerpt explores the activation functions that are critical to making MLPs non-linear, including the ReLU, sigmoid, and tanh functions. It also highlights the importance of activation functions in optimization and discusses various activation function variations, such as pReLU and Swish. Finally, the excerpt touches on the concept of universal approximators, demonstrating that MLPs can learn any function given enough hidden units, but emphasizes that deeper networks can be more efficient.
Read more here: https://d2l.ai/chapter_multilayer-perceptrons/mlp.html
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