#Neural Networks Introduction ##So what are neural networks?? ###Associationism
- Humans learn through association ###Connectionism
- The information is in the connections ####Bain’s Idea 1: Neural Groupings
- Neurons excite and stimulate each other
- Different combinations of inputs can result in different outputs
- Different intensities of activation of A lead to the differences in when X and Y are activated
####Bain’s Idea 2: Making Memories
###Connectionist Machines
Neurons connect to other neurons.The processing/capacity of the brain is a function of these connections All world knowledge is stored in the connections between the elements
- Neural networks are connectionist machines
- The machine has many non-linear processing units
- Connections may also define memory
###The Universal Model Multi-layer Perceptron
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Neural networks began as computational models of the brain
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Neural network models are connectionist machines ~ The comprise networks of neural units
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Multi-layer perceptrons can model arbitrarily complex Boolean functions
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Multi-layer perceptrons are connectionist computational models – Individual perceptrons are computational equivalent of neurons – The MLP is a layered composition of many perceptrons
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MLPs can model Boolean functions – Individual perceptrons can act as Boolean gates – Networks of perceptrons are Boolean functions
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MLPs are Boolean machines – They represent Boolean functions over linear boundaries – They can represent arbitrary decision boundaries – They can be used to classify data
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MLPs are classification engines – They can identify classes in the data – Individual perceptrons are feature detectors – The network will fire if the combination of the detected basic features matches an “acceptable” pattern for a desired class of signal
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MLP can also model continuous valued functions
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MLP can Represent probability distributions – Over integer, real and complex-valued domains – MLPs can model both a posteriori and a priori distributions of data • A posteriori conditioned on other variables – MLPs can generate data from complicated,or even unknown distributions
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The network is a function
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Interesting AI tasks are functions that can be modelled by the network