andy
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Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json Food/Meal Plan.md Lab/Linux/Alpine.md Lab/Linux/KDE.md Lab/Scratch Domain.md Lab/Windows/Active Directory.md Languages/Spanish/README.md Languages/Spanish/Spanish.md Money/Accounts.md Money/Monthly/23-04.md Money/Monthly/23-05.md Money/Monthly/23-06.md Projects/Mixonomer.md Projects/NoteCrawler.md Projects/Projects.md Projects/README.md Projects/Selector.md Projects/To Do App.md Projects/img/selector-arch.png STEM/AI/Classification/Supervised/SVM.md STEM/AI/Neural Networks/CNN/FCN/Super-Resolution.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/GAN/cGAN.md STEM/AI/Neural Networks/CNN/Interpretation.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/MLP/MLP.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/RNN/Representation Learning.md STEM/AI/Pattern Matching/Markov/Markov.md STEM/AI/Searching/Informed.md STEM/AI/Searching/README.md STEM/AI/Searching/Searching.md STEM/AI/Searching/Uninformed.md STEM/CS/Languages/Javascript.md STEM/CS/Languages/Python.md STEM/CS/Languages/dotNet.md STEM/CS/Resources.md STEM/IOT/Cyber-Physical Systems.md STEM/IOT/Networking/Networking.md STEM/IOT/Networking/README.md STEM/IOT/Software Services.md STEM/img/cyberphysical-social-data.png STEM/img/cyberphysical-system-types.png STEM/img/cyberphysical-systems.png STEM/img/depth-first-cons.png STEM/img/depth-first.png STEM/img/iot-mesh-network.png STEM/img/iot-network-radar.png STEM/img/iot-network-types 1.png STEM/img/iot-network-types.png STEM/img/markov-start-end-matrix.png STEM/img/markov-start-end-probs.png STEM/img/markov-start-end.png STEM/img/markov-state-duration.png STEM/img/markov-state.png STEM/img/markov-weather.png STEM/img/search-bidirectional.png STEM/img/search-breadth-first.png STEM/img/search-lim-goal.png STEM/img/search-lim1.png STEM/img/search-lim2.png STEM/img/search-lim3-2.png STEM/img/search-lim3.png STEM/img/search-lim4.png STEM/img/searching-graph-tree.png STEM/img/searching-graph.png Work/Freelancing.md |
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| Back-Propagation.md | ||
| Decision Boundary.md | ||
| MLP.md | ||
| README.md | ||
- Feedforward
- 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
Universal Approximation Theory
A finite feedforward MLP with 1 hidden layer can in theory approximate any mathematical function
- In practice not trainable with BP
Weight Matrix
- Use matrix multiplication for layer output
- TLU is hard limiter
o_1too_4must all be one to overcome -3.5 bias and force output to 1
- Can generate a non-linear decision boundary