参考资料:

在线免费书籍 http://neuralnetworksanddeeplearning.com/chap1.html


 Chapter 1

1.  perceptron 感知机

  • it's a device that makes decisions by weighing up evidence. Just single output.
  • inputs 0 or 1(with weights),compared to threshold, then output 0 or 1.

神经网络和Deep Learning 其中,   神经网络和Deep Learning; 

  • threshold could be simplified as bias;

神经网络和Deep Learning 其中 b= - threshold, is called bias.

  • layers:input layer,hidden layer,output layer

神经网络和Deep Learning

                        hidden layers(not input and not output)

 

 2. Sigmoid neuron

形式类似于Perceptron, 但是输入输出的值略有变化。这样微小的权重变化,不会引起大的output变化;而Perceptron无法保证。

(small changes in the output from the neuron

  • input :   [0,1] 
  • output:  神经网络和Deep Learning=神经网络和Deep Learning, 其中σ 为sigmoid函数,作用于output.

 

 

 

 

 

 

 

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