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Backpropagation
Used to train multilayer feedforward networks
Assumes a continuous activation function
Backpropagation
Used to train multilayer feedforward networks
Assumes a continuous activation function
Delta rule
Backpropagation Delta rule
Perceptron update rule was:
Backprop update rule is:
€
Δw = c(desired − sign(actual))x
€
Δw = c(error)x
Backpropagation Delta rule
Error of an output node:
€
error j = (1−output j2)(desired j − actual j )
When not to use Feedforward net
If you can draw a flow chart or formula
If a piece of hardware or software already exists that does what you want
When not to use Feedforward net
If you can draw a flow chart or formula
If a piece of hardware or software already exists that does what you want
If you want to functionality to evolve
When not to use Feedforward net
If you can draw a flow chart or formula
If a piece of hardware or software already exists that does what you want
If you want to functionality to evolvePrecise answers are required
When not to use Feedforward net
If you can draw a flow chart or formula
If a piece of hardware or software already exists that does what you want
If you want to functionality to evolvePrecise answers are requiredThe problem could be described in a
lookup table
When to use feedforward net
You can define a correct answerYou have a lot of training data with
examples of right and wrong answers
When to use feedforward net
You can define a correct answerYou have a lot of training data with
examples of right and wrong answers
You have lots of data but can’t figure how to map it to output
When to use feedforward net
You can define a correct answerYou have a lot of training data with
examples of right and wrong answers
You have lots of data but can’t figure how to map it to output
The problem is complex but solvable
When to use feedforward net
You can define a correct answerYou have a lot of training data with
examples of right and wrong answers
You have lots of data but can’t figure how to map it to output
The problem is complex but solvableThe solution is fuzzy or might change
slightly
Examples
Jonathan McCabe’sNervous States 2006Each pixel is the Output state of aNeural network givenDifferent inputs
Examples
2007 Phillip StearnsAANN: Artificial Analog Neural Network
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