andy
5a592c8c7c
Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json STEM/AI/Ethics.md STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/CNN/CNN.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/Neural Networks.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/RNN/LSTM.md STEM/AI/Neural Networks/RNN/RNN.md STEM/AI/Neural Networks/RNN/VQA.md STEM/AI/Neural Networks/SLP/SLP.md STEM/AI/Neural Networks/Training.md STEM/AI/Neural Networks/Transformers/Attention.md STEM/AI/Neural Networks/Transformers/LLM.md STEM/AI/Neural Networks/Transformers/Transformers.md STEM/Signal Proc/System Classes.md STEM/img/back-prop-equations.png STEM/img/back-prop-weight-changes.png STEM/img/back-prop1.png STEM/img/back-prop2.png STEM/img/cnn+lstm.png STEM/img/deep-digit-classification.png STEM/img/deep-loss-function.png STEM/img/llm-family-tree.png STEM/img/lstm-slp.png STEM/img/lstm.png STEM/img/matrix-dot-product.png STEM/img/ml-dl.png STEM/img/photo-tensor.png STEM/img/relu.png STEM/img/rnn-input.png STEM/img/rnn-recurrence.png STEM/img/slp-arch.png STEM/img/threshold-activation.png STEM/img/transformer-arch.png STEM/img/vqa-block.png
787 B
787 B
- 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
!
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
!
!
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