Andy Pack
efa7a84a8b
Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json Languages/Spanish/Spanish.md STEM/AI/Classification/Classification.md STEM/AI/Classification/Decision Trees.md STEM/AI/Classification/Logistic Regression.md STEM/AI/Classification/Random Forest.md STEM/AI/Classification/Supervised/SVM.md STEM/AI/Classification/Supervised/Supervised.md STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/SLP/Perceptron Convergence.md
102 lines
3.2 KiB
Markdown
102 lines
3.2 KiB
Markdown
---
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tags:
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- ai
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---
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# Linearity
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- Neurons can be linear or non-linear
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- Network of non-linear neurons is non-linear
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- Non-linearity is distributed
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- Helpful if target signals are generated non-linearly
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# Input-Output Mapping
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- Map input signal to desired response
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- [Supervised](../Learning.md#Supervised) learning
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- Similar to non-parametric statistical inference
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- Non-parametric as in no prior assumptions
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- No probabilistic model
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# Adaptivity
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- Synaptic weights
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- Can be easily retrained
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- Stationary environment
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- Essential statistics can be learned
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- Model can then be frozen
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- Non-stationary environments
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- Can change weights in real-time
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- In general, more adaptive = more robust
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- Not always though
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- Short time-constant system may be thrown by short-time spurious disturbances
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- Stability-plasticity dilemma
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- Not equipped to track statistical variations
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- Adaptive system
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- Linear adaptive filter
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- Linear combiner
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- Single neuron operating in linear mode
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- Mature applications
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- Nonlinear adaptive filters
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- Less mature
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- Environments typically considered pseudo-stationary
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- Speech stationary over short windows
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- Retrain network at regular intervals to account for fluctuations
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- E.g. stock market
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- Train network on short time window
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- Add new data and pop old
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- Slide window
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- Retrain network
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# Evidential Response
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- Decisions are made with evidence not just declared
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- Confidence value
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# Contextual Information
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- [Knowledge](Neural%20Networks.md#Knowledge) represented by structure and activation weight
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- Any neuron can be affected by global activity
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- Contextual information handled naturally
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# Fault Tolerance
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- Hardware implementations
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- Performance degrades gracefully with adverse conditions
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- If some of it breaks, it won't cause the whole thing to break
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- Like a real brain
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# VLSI Implementability
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- Very large-scale integration
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- Chips with millions of transistors (MOS)
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- E.g. microprocessor, memory chips
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- Massively parallel nature
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- Well suited for VLSI
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# Uniformity in Analysis
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- Are domain agnostic in application
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- Analysis methods are the same
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- Can share theories, learning algorithms
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# Neurobiological Analogy
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- Design analogous to brain
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- Already a demonstrable fault-tolerant, powerful, fast, parallel processor
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- To slight changes
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- Rotation of target in images
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- Doppler shift in radar
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- Network needs to be invariant to these transformations
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# Invariance
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1. Invariance by Structure
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- Synaptic connections created so that transformed input produces same output
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- Set same weight for neurons of some geometric relationship to image
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- Same distance from centre e.g.
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- Number of connections becomes prohibitively large
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2. Invariance by Training
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- Train on different views/transformations
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- Take advantage of inherent pattern #classification abilities
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- Training for invariance for one object is not necessarily going to train other classes for invariance
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- Extra load on network to do more training
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- Exacerbated with high dimensionality
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3. Invariant Feature Space
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- Extract invariant features
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- Use network as classifier
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- Relieves burden on network to achieve invariance
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- Complicated decision boundaries
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- Number of features applied to network reduced
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- Invariance ensured
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- Required prior knowledge |