two important types of artificial neuron :the perceptron and the sigmoid neuron
Perceptrons
感知机的输入个数不限,每个输入的取值都是二元的(0或1,这点不确定，后续确认下)，输出是0或1.
Sigmoid neuron
Sigmoid neurons are similar to perceptrons, but modified so that small changes in their weights and bias cause only a small change in their output. That's the crucial fact which will allow a network of sigmoid neurons to learn.
sigmoid neuron 输入:these inputs can take on any values between 0 and 1.
sigmoid neuron与perceptrons的相似点：当z = w . x + b ->正无穷时,sigmoid neuron的输出趋向于1.当z = w . x + b ->负无穷时,sigmoid neuron的输出趋向于0.而感知机的输出就是0或1.
The smoothness of σ (sigmoid函数值的平滑性) means that small changes in the weights and in the bias will produce a small change in the output from the neuron.
上面公式如何推导出来？
后续的笔记直接写在纸上了
The architecture of neural networks
A simple network to classify handwritten digits
Learning with gradient descent
Implementing our network to classify digits
Toward deep learning