diff --git a/neural_network/2_hidden_layers_neural_network.py b/neural_network/2_hidden_layers_neural_network.py
new file mode 100644
index 000000000..b924a8593
--- /dev/null
+++ b/neural_network/2_hidden_layers_neural_network.py
@@ -0,0 +1,112 @@
+import numpy
+
+class NeuralNetwork:
+
+    def __init__(self, input_array, output_array):
+        '''
+        input_array : input values for training the neural network.
+        output_array : expected output values of the given inputs.
+        '''
+        self.input = input_array
+        #Initial weights are assigned randomly where first argument is the number of nodes in previous layer
+        #and second argument is the number of nodes in the next layer.
+
+        #random initial weights for the input layer
+        #self.input.shape[1] is used to reprsent number of nodes in input layer
+        #first hidden layer consists of 4 nodes
+        self.weights1 = numpy.random.rand(self.input.shape[1],4)
+
+        #random initial weights for the first hidden layer
+        #first hidden layer has 4 nodes
+        #second hidden layer has 3 nodes
+        self.weights2 = numpy.random.rand(4,3)
+
+        #random inital weights for the second hidden layer
+        #second hidden layer has 3 nodes
+        #output layer has 1 node
+        self.weights3 = numpy.random.rand(3,1)
+
+        self.y = output_array
+        self.output = numpy.zeros(output_array.shape)
+
+    def feedforward(self):
+        '''
+        feedforward propagation using sigmoid activation function between layers
+        return the last layer of the neural network
+        '''
+        #layer1 is the layer connecting the input nodes with the first hidden layer nodes
+        self.layer1 = sigmoid(numpy.dot(self.input, self.weights1))
+
+        #layer2 is the layer connecting the first hidden set of nodes with the second hidden set of nodes
+        self.layer2 = sigmoid(numpy.dot(self.layer1, self.weights2))
+
+        #layer3 is the layer connecting second hidden layer with the output node
+        self.layer3 = sigmoid(numpy.dot(self.layer2,self.weights3))
+
+        return self.layer3
+
+    def back_propagation(self):
+        '''
+        backpropagating between the layers using sigmoid derivative and loss between layers
+        updates the weights between the layers
+        '''
+
+        updated_weights3 = numpy.dot(self.layer2.T,2*(self.y-self.output)*sigmoid_derivative(self.output))
+        updated_weights2 = numpy.dot(self.layer1.T, numpy.dot(2*(self.y -self.output)*sigmoid_derivative(self.output), self.weights3.T)*sigmoid_derivative(self.layer2))
+        updated_weights1 = numpy.dot(self.input.T, numpy.dot(numpy.dot(2*(self.y -self.output)*sigmoid_derivative(self.output), self.weights3.T)*sigmoid_derivative(self.layer2),self.weights2.T)*sigmoid_derivative(self.layer1))
+
+        self.weights1 += updated_weights1
+        self.weights2 += updated_weights2
+        self.weights3 += updated_weights3
+
+    def train(self, output, iterations):
+        '''
+        output : required for calculating loss
+        performs the feeding and back propagation process for the given number of iterations
+        every iteration will update the weights of neural network
+        '''
+        for iteration in range(1,iterations+1):
+            self.output = self.feedforward()
+            self.back_propagation()
+            print("Iteration %s "%iteration,"Loss: " + str(numpy.mean(numpy.square(output - self.feedforward()))))
+
+    def predict(self, input):
+        '''
+        predict output for the given input values
+        '''
+        self.array = input
+        self.layer1 = sigmoid(numpy.dot(self.array, self.weights1))
+        self.layer2 = sigmoid(numpy.dot(self.layer1, self.weights2))
+        self.layer3 = sigmoid(numpy.dot(self.layer2,self.weights3))
+        return self.layer3
+
+def sigmoid(value):
+    '''
+    applies sigmoid activation function
+    return normalized values
+    '''
+    return 1/(1+numpy.exp(-value))
+
+def sigmoid_derivative(value):
+    '''
+    returns derivative of the sigmoid value
+    '''
+    return sigmoid(value)*(1-sigmoid(value))
+
+
+if __name__ == "__main__":
+
+    #input values
+    input = numpy.array(([0,0,0],[0,0,1],[0,1,0],[0,1,1],
+    [1,0,0],[1,0,1],[1,1,0],[1,1,1]),dtype=float)
+
+    #true output for the given input values
+    output = numpy.array(([0],[1],[1],[0],
+    [1],[0],[0],[1]),dtype=float)
+
+    #calling neural network class
+    Neural_Network = NeuralNetwork(input_array= input, output_array= output)
+
+    #calling training function
+    Neural_Network.train(output= output, iterations= 1000)
+    print(Neural_Network.predict([0,1,1]))
\ No newline at end of file