Lecture_17 - Learning in Artificial Neural Networks...

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Unformatted text preview: Learning in Artificial Neural Networks Perceptrons and the XOR Problem Recurrent Networks Dynamical Systems Multi-layer Networks Perceptrons and the XOR Problem Perceptrons The Delta Rule Input Output Perceptrons [0 1 0 1] * .3 .2 .1 .4 .2 -.2 .3 -.1 = [.4 .3] XOR Problem Input 0 0 0 1 1 0 1 1 Output 1 1 not linearly separable XOR Problem Linear Separability 1 1 Input1 Input2 1 1 Perceptrons for the XOR Problem Learning in Artificial Neural Networks Perceptrons and the XOR Problem Recurrent Networks Dynamical Systems Multi-layer Networks Multi-layer Networks Backpropagation Networks for the XOR Problem 1-10 +10 Logistic Function Backpropagation Networks for the XOR Problem Bias Weights Momentum Simulated Annealing Learning Rate D i m e n s i o n 1 D i m e n s i o n 2 Error Competitive Hebbian Learning Competitive Hebbian Learning Layer D Layer C Layer B Layer A Development of the visual system Learning in Artificial Neural Networks...
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Lecture_17 - Learning in Artificial Neural Networks...

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