1 神经网络概览 Neural network overview

You can form a neural network by stacking together a lot of little sigmoid units
whereas previously this node corresponds to two steps of calculations
The first compute the z value, second it computes this ‘a’ value, in this neural network this stack of nodes will correspond to a ‘z’ like calculation like this , as well as an ‘a’ calculation like that

2 神经网络表示 Neural network representation

we have the input features x1 x2 x3 stacked vertically and this is called the input layer of the neural network

hidden layer of the neural network

In a neural network you train with supervised learning, the training set contains values of inputs x as well as the target outputs y
So the term hidden layer refers to the fact that in a training set the true values for these nodes in the middle are not observed.
That is you don’t see what they should be in the training set, you can see the input and output values

when we count layers in neural networks , we don’t count the input layer
so the hidden layer is layer 1
and the output layer is layer 2
the hidden layer and the output layer will have parameters associated with it
so the hidden layer will have associated with their parameters w and b

3 计算神经网络的输出 Computing a Neural Network’s Output

In this video let’s go through the details of exactly how this neural network computes outputs.
what you see is that is like logistic regression, but repeat a lot of times

take these four equations vectorize