36 lines
1.5 KiB
Markdown
36 lines
1.5 KiB
Markdown
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- Massively parallel, distributed processor
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- Natural propensity for storing experiential knowledge
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# Resembles Brain
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- Knowledge acquired from by network through learning
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- Interneuron connection strengths store acquired knowledge
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- Synaptic weights
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![[slp-arch.png]]
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A neural network is a directed graph consisting of nodes with interconnecting synaptic and activation links, and is characterised by four properties
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1. 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
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2. The synaptic links of a neuron weight their respective input signals
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3. The weighted sum of the input signals defines the induced local field of the neuron in question
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4. The activation link squashes the induced local field of the neuron to produce an output
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# Knowledge
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*Knowledge refers to stored information or models used by a person or machine to interpret, predict, and appropriately respond to the outside world*
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Made up of:
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1. The known world state
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- Represented by facts about what is and what has been known
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- Prior information
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2. Observations of the world
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- Usually inherently noisy
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- Measurement error
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- Pool of information used to train
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- Can be labelled or not
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- (Un-)Supervised
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*Knowledge representation of the surrounding environment is defined by the values taken on by the free parameters of the network*
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- Synaptic weights and biases
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