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stem/AI/Neural Networks/Architectures.md
andy 4cc2e79866 vault backup: 2023-05-31 22:21:56 
Affected files:
.obsidian/global-search.json
.obsidian/workspace.json
Health/Alexithymia.md
Health/BWS.md
STEM/AI/Neural Networks/Activation Functions.md
STEM/AI/Neural Networks/Architectures.md
STEM/AI/Neural Networks/CNN/CNN.md
STEM/AI/Neural Networks/MLP/Back-Propagation.md
STEM/AI/Neural Networks/Transformers/Attention.md
STEM/CS/Calling Conventions.md
STEM/CS/Languages/Assembly.md
2023-05-31 22:21:56 +01:00

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Single-Layer Feedforward

  • Acyclic
  • Count output layer, no computation at input

feedforward

Multilayer Feedforward

  • Hidden layers
    • Extract higher-order statistics
    • Global perspective
    • Helpful with large input layer
  • Fully connected
    • Every neuron is connected to every neuron adjacent layers
  • Below is a 10-4-2 network multilayerfeedforward

Recurrent

  • At least one feedback loop

  • Below has no self-feedback recurrent recurrentwithhn

  • Above has hidden neurons