Sun’iy intellekt va neyron tarmoqlari”fanidan sun’iy neyron tarmoqlari modeli

Foydalanilgan internet saytlari.

1. 
   https://en.wikipedia.org/wiki/Artificial_neural_network
   
2. 
   https://www.geeksforgeeks.org/implementing-models-of-artificial-neural-network/
   
3. 
   https://towardsdatascience.com/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6#106c
   
4. 
   https://www.ibm.com/cloud/learn/neural-networks
   
5. 
   https://medium.com/analytics-vidhya/artificial-neural-networks-an-intuitive-approach-part-1-890efac210f0
   
6. 
   https://www.mygreatlearning.com/blog/types-of-neural-networks/
   
7. 
   https://towardsdatascience.com/a-journey-through-neural-networks-part-1-artificial-neural-network-and-perceptron-e970614b9cc7
   
8. 
   https://viso.ai/deep-learning/artificial-neural-network/
   
9. 
   https://medium.com/datasciencearth/basic-structure-of-artificial-neural-networks-9aef29df9d
   
10. 
    https://www.geeksforgeeks.org/introduction-to-artificial-neutral-networks/
    
11. 
    https://www.javatpoint.com/artificial-neural-network
    
12. 
    https://brilliant.org/wiki/artificial-neural-network/
    
13. 
    https://natureofcode.com/book/chapter-10-neural-networks/
    

Ilova

2 qatlamli neyron tarmoq:

import numpy as np

# sigmoid function

def nonlin(x,deriv=False):
if(deriv==True):
return x*(1-x)
return 1/(1+np.exp(-x))
# input dataset
X = np.array([ [0,0,1],
[0,1,1],
[1,0,1],
[1,1,1] ])
# output dataset
y = np.array([[0,0,1,1]]).T

# seed random numbers to make calculation

# deterministic (just a good practice)
np.random.seed(1)

# initialize weights randomly with mean 0

syn0 = 2*np.random.random((3,1)) - 1

for iter in xrange(10000):

# forward propagation

l0 = X
l1 = nonlin(np.dot(l0,syn0))

# how much did we miss?

l1_error = y - l1

# multiply how much we missed by the

# slope of the sigmoid at the values in l1
l1_delta = l1_error * nonlin(l1,True)

# update weights

syn0 += np.dot(l0.T,l1_delta)

print "Output After Training:"

print l1
Treningdan keyingi natijalar:
[[ 0.00966449]
[0,00786506]
[0.99358898]
[0.99211957]]