diff --git a/Neural Networks and Deep Learning/Building your Deep Neural Network - Step by Step.ipynb b/Neural Networks and Deep Learning/Building your Deep Neural Network - Step by Step.ipynb
index 349f6c5..002d426 100755
--- a/Neural Networks and Deep Learning/Building your Deep Neural Network - Step by Step.ipynb	
+++ b/Neural Networks and Deep Learning/Building your Deep Neural Network - Step by Step.ipynb	
@@ -1231,15 +1231,13 @@
     "    \n",
     "    # Initializing the backpropagation\n",
     "    ### START CODE HERE ### (1 line of code)\n",
-    "    dAL = dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n",
+    "    dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n",
     "    ### END CODE HERE ###\n",
     "    \n",
     "    # Lth layer (SIGMOID -> LINEAR) gradients. Inputs: \"AL, Y, caches\". Outputs: \"grads[\"dAL\"], grads[\"dWL\"], grads[\"dbL\"]\n",
     "    ### START CODE HERE ### (approx. 2 lines)\n",
     "    current_cache = caches[-1]\n",
-    "    grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_backward(sigmoid_backward(dAL, \n",
-    "                                                                                                        current_cache[1]), \n",
-    "                                                                                       current_cache[0])\n",
+    "    grads[\"dA\" + str(L-1)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_activation_backward(dAL, current_cache, activation="sigmoid")\n",
     "    ### END CODE HERE ###\n",
     "    \n",
     "    for l in reversed(range(L-1)):\n",
@@ -1247,7 +1245,7 @@
     "        # Inputs: \"grads[\"dA\" + str(l + 2)], caches\". Outputs: \"grads[\"dA\" + str(l + 1)] , grads[\"dW\" + str(l + 1)] , grads[\"db\" + str(l + 1)] \n",
     "        ### START CODE HERE ### (approx. 5 lines)\n",
     "        current_cache = caches[l]\n",
-    "        dA_prev_temp, dW_temp, db_temp = linear_backward(sigmoid_backward(dAL, caches[1]), caches[0])\n",
+    "        dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA" + str(L-1)], current_cache, activation="relu")\n",
     "        grads[\"dA\" + str(l + 1)] = dA_prev_temp\n",
     "        grads[\"dW\" + str(l + 1)] = dW_temp\n",
     "        grads[\"db\" + str(l + 1)] = db_temp\n",