andy
f65496a79f
Affected files: .obsidian/workspace.json STEM/AI/Neural Networks/MLP.md STEM/AI/Neural Networks/MLP/Activation Functions.md STEM/AI/Neural Networks/MLP/Back-Propagation.md STEM/AI/Neural Networks/MLP/Decision Boundary.md STEM/img/hidden-neuron-decision.png STEM/img/mlp-non-linear-decision.png STEM/img/mlp-xor-2.png STEM/img/mlp-xor.png STEM/img/sigmoid.png STEM/img/tlu.png
22 lines
765 B
Markdown
22 lines
765 B
Markdown
- Feed-forward
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- Single hidden layer can learn any function
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- Universal approximation theorem
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- Each hidden layer can operate as a different feature extraction layer
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- Lots of weights to learn
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- [[Back-Propagation]] is supervised
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![[mlp-arch.png]]
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# Universal Approximation Theory
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A finite feed-forward MLP with 1 hidden layer can in theory approximate any mathematical function
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- In practice not trainable with [[Back-Propagation|BP]]
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![[activation-function.png]]
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![[mlp-arch-diagram.png]]
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## Weight Matrix
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- Use matrix multiplication for layer output
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- TLU is hard limiter
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![[tlu.png]]
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- $o_1$ to $o_4$ must all be one to overcome -3.5 bias and force output to 1
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![[mlp-non-linear-decision.png]]
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- Can generate a non-linear decision boundary |