stem/AI/Neural Networks/MLP/MLP.md
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Markdown

- Feed-forward
- 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.png]]
# Universal Approximation Theory
A finite feed-forward MLP with 1 hidden layer can in theory approximate any mathematical function
- In practice not trainable with [[Back-Propagation|BP]]
![[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|decision boundary]]