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andy b30da1d29c vault backup: 2023-06-01 08:11:37 
Affected files:
.obsidian/graph.json
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Money/Assets/Derivative.md
STEM/AI/Neural Networks/CNN/Examples.md
STEM/AI/Neural Networks/Deep Learning.md
STEM/AI/Neural Networks/MLP/Decision Boundary.md
STEM/CS/Languages/dotNet.md
STEM/Semiconductors/Equations.md
Tattoo/Engineering.md
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Back-Propagation.md vault backup: 2023-05-31 22:51:45 2023-05-31 22:51:45 +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: 2023-05-27 22:17:56 2023-05-27 22:17:56 +01:00
README.md vault backup: 2023-05-31 21:29:04 2023-05-31 21:29:04 +01:00

README.md

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

!mlp-arch.png

Universal Approximation Theory

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

!activation-function.png !mlp-arch-diagram.png

Weight Matrix

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