diff --git a/doc/HandWrittenNotes/2024/NotesOctober7.pdf b/doc/HandWrittenNotes/2024/NotesOctober7.pdf
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diff --git a/doc/src/week41/programs/perceptron.py b/doc/src/week41/programs/perceptron.py
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+import numpy as np
+# We use the Sigmoid function as activation function
+def sigmoid(z):
+    return 1.0/(1.0+np.exp(-z))
+
+def forwardpropagation(x):
+    # weighted sum of inputs to the hidden layer
+    z_1 = np.matmul(x, w_1) + b_1
+    # activation in the hidden layer
+    a_1 = sigmoid(z_1)
+    # weighted sum of inputs to the output layer
+    z_2 = np.matmul(a_1, w_2) + b_2
+    a_2 = z_2
+    return a_1, a_2
+
+def backpropagation(x, y):
+    a_1, a_2 = forwardpropagation(x)
+    # parameter delta for the output layer, note that a_2=z_2 and its derivative wrt z_2 is just 1
+    delta_2 = a_2 - y
+    print(0.5*((a_2-y)**2))
+    # delta for  the hidden layer
+    delta_1 = np.matmul(delta_2, w_2.T) * a_1 * (1 - a_1)
+    # gradients for the output layer
+    output_weights_gradient = np.matmul(a_1.T, delta_2)
+    output_bias_gradient = np.sum(delta_2, axis=0)
+    # gradient for the hidden layer
+    hidden_weights_gradient = np.matmul(x.T, delta_1)
+    hidden_bias_gradient = np.sum(delta_1, axis=0)
+    return output_weights_gradient, output_bias_gradient, hidden_weights_gradient, hidden_bias_gradient
+
+
+# ensure the same random numbers appear every time
+np.random.seed(0)
+# Input variable
+x = np.array([4.0],dtype=np.float64)
+# Target values
+y = 5*x*x+2*x+1.0 
+
+# Defining the neural network, only scalars here
+n_inputs = x.shape
+n_features = 1
+n_hidden_neurons = 1
+n_outputs = 1
+
+# Initialize the network
+# weights and bias in the hidden layer
+w_1 = np.random.randn(n_features, n_hidden_neurons)
+b_1 = np.zeros(n_hidden_neurons) + 0.01
+
+# weights and bias in the output layer
+w_2 = np.random.randn(n_hidden_neurons, n_outputs)
+b_2 = np.zeros(n_outputs) + 0.01
+
+print("MSE as function of iterations")
+eta = 0.1
+for i in range(50):
+    # calculate gradients
+    derivW2, derivB2, derivW1, derivB1 = backpropagation(x, y)
+    # update weights and biases
+    w_2 -= eta * derivW2
+    b_2 -= eta * derivB2
+    w_1 -= eta * derivW1
+    b_1 -= eta * derivB1