andy
33ac3007bc
Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/CNN/FCN/FlowNet.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/Deep Learning.md STEM/AI/Neural Networks/MLP/Back-Propagation.md STEM/AI/Neural Networks/MLP/MLP.md STEM/AI/Neural Networks/Transformers/Attention.md STEM/CS/ABI.md STEM/CS/Calling Conventions.md STEM/CS/Code Types.md STEM/CS/Language Binding.md STEM/img/am-regulariser.png STEM/img/skip-connections.png
837 B
837 B
- 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
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Universal Approximation Theory
A finite Architectures MLP with 1 hidden layer can in theory approximate any mathematical function
- In practice not trainable with Back-Propagation
!
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Weight Matrix
- Use matrix multiplication for layer output
- TLU is hard limiter
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o_1too_4must all be one to overcome -3.5 bias and force output to 1 !
- Can generate a non-linear Decision Boundary