# Neural Network

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1.(x1,x2,x3) -> Perceptron -> output

2.Input: x1,x2,x3

3.Weights were introduced as w1, w2, ..., real numbers expressing the importance of the respective inputs to the output.

4.output=0 when sum(wjxj)<=threshold,
output=1 when sum(wjxj)>threshold

* Perceptron: 感知器
* 感知器的輸入為x1, x2, x3
* 感知器的輸入加上權重代表每個輸入的重要性(影響力)
* output = 0 or 1

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[reference 1](http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons)