Examples & Intuition ll
Examples & Intuition ll
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how neural networks can compute non-linear hypothesis
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example of negation
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put large number to negate each other
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0 0 = - 30(1)
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0 1 = 10(0)
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1 0 = 10 (0)
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1 1 = 10 (0)
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xnor need non-linear decision boundaries
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xnor outputs 1 when either x1 and x2 are the same
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this is why neural networks can compute more and more complicated function
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more and more complex hidden layer for recognizing 9
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by Yann LeCun of New York University that some said legend at neural networks, that some of the techniques are widely used throughout world's application
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more and more complex features to provide the final parameters that logistic regression can compute