andy
4cc2e79866
Affected files: .obsidian/global-search.json .obsidian/workspace.json Health/Alexithymia.md Health/BWS.md STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/Architectures.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/MLP/Back-Propagation.md STEM/AI/Neural Networks/Transformers/Attention.md STEM/CS/Calling Conventions.md STEM/CS/Languages/Assembly.md |
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| .. | ||
| CNN | ||
| CV | ||
| MLP | ||
| RNN | ||
| SLP | ||
| Transformers | ||
| Activation Functions.md | ||
| Architectures.md | ||
| Deep Learning.md | ||
| Neural Networks.md | ||
| Properties+Capabilities.md | ||
| README.md | ||
| Training.md | ||
| Weight Init.md | ||
- Massively parallel, distributed processor
- Natural propensity for storing experiential knowledge
Resembles Brain
- Knowledge acquired from by network through learning
- Interneuron connection strengths store acquired knowledge
- Synaptic weights
!
A neural network is a directed graph consisting of nodes with interconnecting synaptic and activation links, and is characterised by four properties
- Each neuron is represented by a set of linear synaptic links, an externally applied bias, and a possibly nonlinear activation link. The bias is represented by a synaptic link connected to an input fixed at +1
- The synaptic links of a neuron weight their respective input signals
- The weighted sum of the input signals defines the induced local field of the neuron in question
- The activation link squashes the induced local field of the neuron to produce an output
Knowledge
Knowledge refers to stored information or models used by a person or machine to interpret, predict, and appropriately respond to the outside world
Made up of:
- The known world state
- Represented by facts about what is and what has been known
- Prior information
- Observations of the world
- Usually inherently noisy
- Measurement error
- Pool of information used to train
- Can be labelled or not
- (Un-)Supervised
Knowledge representation of the surrounding environment is defined by the values taken on by the free parameters of the network
- Synaptic weights and biases