Neural Networks, Gradient Descent.
Randomly initalised weights weight1 = np.random.random((3,4)) weight2 = np.random.random((4,1)) # Activation to layer 0 is taken as input x a0 = x iterations = 10000 for iter in range(0,iterations): ## The forward pass z1= np.dot(x,weight1) a1 = sigmoid(z1) z2= np.dot(a1,weight2) a2 = sigmoid(z2)