by Vivek p.a
Last Updated July 13, 2019 01:26 AM

I have been trying to code a neural network from scratch and have watched a couple of videos to see how it is implemented.

So I came across this guide that builds a simple neural network in Python.

```
X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
y = np.array([[0,1,1,0]]).T
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1
for j in xrange(60000):
l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
l2_delta = (y - l2)*(l2*(1-l2))
l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
syn1 += l1.T.dot(l2_delta)
syn0 += X.T.dot(l1_delta)
```

I find the last 2 lines confusing shouldn't it be `syn1 -= l1.T.dot(l2_delta)`

and `syn0 -= X.T.dot(l1_delta)`

.

I thought that in gradient descent you subtract the slope, but it seems like here it is added. Is this gradient ascent?

Can someone please explain how the last 2 lines work?

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