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stem/AI/Neural Networks/MLP
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andy 7bc4dffd8b vault backup: 2023-06-06 11:48:49 
Affected files:
STEM/AI/Neural Networks/CNN/Examples.md
STEM/AI/Neural Networks/CNN/FCN/FCN.md
STEM/AI/Neural Networks/CNN/FCN/ResNet.md
STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md
STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md
STEM/AI/Neural Networks/CNN/GAN/GAN.md
STEM/AI/Neural Networks/CNN/Interpretation.md
STEM/AI/Neural Networks/CNN/UpConv.md
STEM/AI/Neural Networks/Deep Learning.md
STEM/AI/Neural Networks/MLP/MLP.md
STEM/AI/Neural Networks/Properties+Capabilities.md
STEM/AI/Neural Networks/SLP/Least Mean Square.md
STEM/AI/Neural Networks/SLP/SLP.md
STEM/AI/Neural Networks/Transformers/Transformers.md
STEM/AI/Properties.md
STEM/CS/Language Binding.md
STEM/CS/Languages/dotNet.md
STEM/Signal Proc/Image/Image Processing.md
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Back-Propagation.md Revert "vault backup: 2023-06-04 22:31:53" 2023-06-04 22:34:31 +01:00
Decision Boundary.md vault backup: 2023-06-01 08:11:37 2023-06-01 08:11:37 +01:00
MLP.md vault backup: 2023-06-06 11:48:49 2023-06-06 11:48:49 +01:00
README.md vault backup: 2023-05-31 21:29:04 2023-05-31 21:29:04 +01:00

README.md

  • Feedforward
  • Single hidden layer can learn any function
    • Universal approximation theorem
  • Each hidden layer can operate as a different feature extraction layer
  • Lots of weights to learn
  • Back-Propagation is supervised

mlp-arch

Universal Approximation Theory

A finite feedforward MLP with 1 hidden layer can in theory approximate any mathematical function

  • In practice not trainable with BP

activation-function mlp-arch-diagram

Weight Matrix

  • Use matrix multiplication for layer output
  • TLU is hard limiter tlu
  • o_1 to o_4 must all be one to overcome -3.5 bias and force output to 1 mlp-non-linear-decision
  • Can generate a non-linear decision boundary