diff --git a/01_mysteries_of_neural_networks/03_numpy_neural_net/Numpy deep neural network.ipynb b/01_mysteries_of_neural_networks/03_numpy_neural_net/Numpy deep neural network.ipynb
index 28f6559..7a94ba5 100644
--- a/01_mysteries_of_neural_networks/03_numpy_neural_net/Numpy deep neural network.ipynb	
+++ b/01_mysteries_of_neural_networks/03_numpy_neural_net/Numpy deep neural network.ipynb	
@@ -191,8 +191,8 @@
     "\n",
     "def relu_backward(dA, Z):\n",
     "    dZ = np.array(dA, copy = True)\n",
-    "    dZ[Z <= 0] = 0;\n",
-    "    return dZ;"
+    "    dZ[Z <= 0] = 0\n",
+    "    return dZ"
    ]
   },
   {
@@ -451,7 +451,7 @@
     "    Y = Y.reshape(Y_hat.shape)\n",
     "    \n",
     "    # initiation of gradient descent algorithm\n",
-    "    dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat));\n",
+    "    dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat))\n",
     "    \n",
     "    for layer_idx_prev, layer in reversed(list(enumerate(nn_architecture))):\n",
     "        # we number network layers from 1\n",
@@ -503,7 +503,7 @@
     "        params_values[\"W\" + str(layer_idx)] -= learning_rate * grads_values[\"dW\" + str(layer_idx)]        \n",
     "        params_values[\"b\" + str(layer_idx)] -= learning_rate * grads_values[\"db\" + str(layer_idx)]\n",
     "\n",
-    "    return params_values;"
+    "    return params_values"
    ]
   },
   {
@@ -750,7 +750,7 @@
    "outputs": [],
    "source": [
     "# Training\n",
-    "params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), NN_ARCHITECTURE, 10000, 0.01)[0]"
+    "params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), NN_ARCHITECTURE, 10000, 0.01)"
    ]
   },
   {