diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
index 8d70ee2b..d5a65861 100644
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -142,9 +142,28 @@
     "    # Apply Dropout\n",
     "    norm3 = tf.nn.dropout(norm3, _dropout)\n",
     "\n",
+    "    # Add a 4th Convolution Layer\n",
+    "    conv4 = conv2d('conv4', norm3, _weights['wc4'], _biases['bc4'])\n",
+    "    # Apply Max Pooling\n",
+    "    pool4 = max_pool('pool4', conv4, k=2)\n",
+    "    # Apply Normalization\n",
+    "    norm4 = norm('norm4', pool4, lsize=4)\n",
+    "    # Apply Dropout\n",
+    "    norm4 = tf.nn.dropout(norm4, _dropout)\n",
+    "\n",
+    "    # Add a 5th Convolution Layer\n",
+    "    conv5 = conv2d('conv5', norm4, _weights['wc5'], _biases['bc5'])\n",
+    "    # Apply Max Pooling\n",
+    "    pool5 = max_pool('pool5', conv5, k=2)\n",
+    "    # Apply Normalization\n",
+    "    norm5 = norm('norm5', pool5, lsize=4)\n",
+    "    # Apply Dropout\n",
+    "    norm5 = tf.nn.dropout(norm5, _dropout)\n",
+    "\n",
     "    # Fully connected layer\n",
-    "    # Reshape conv3 output to fit dense layer input\n",
-    "    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) \n",
+    "    # Update the reshape input for the fully connected layer to reflect the output of norm5\n",
+    "    dense1 = tf.reshape(norm5, [-1, _weights['wd1'].get_shape().as_list()[0]]) \n",
+    "\n",
     "    # Relu activation\n",
     "    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')\n",
     "    \n",
@@ -158,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -169,6 +188,8 @@
     "    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),\n",
     "    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),\n",
     "    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),\n",
+    "    'wc4': tf.Variable(tf.random_normal([3, 3, 256, 384])),\n",
+    "    'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])),\n",
     "    'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),\n",
     "    'wd2': tf.Variable(tf.random_normal([1024, 1024])),\n",
     "    'out': tf.Variable(tf.random_normal([1024, 10]))\n",
@@ -177,6 +198,8 @@
     "    'bc1': tf.Variable(tf.random_normal([64])),\n",
     "    'bc2': tf.Variable(tf.random_normal([128])),\n",
     "    'bc3': tf.Variable(tf.random_normal([256])),\n",
+    "    'bc4': tf.Variable(tf.random_normal([384])),\n",
+    "    'bc5': tf.Variable(tf.random_normal([256])),\n",
     "    'bd1': tf.Variable(tf.random_normal([1024])),\n",
     "    'bd2': tf.Variable(tf.random_normal([1024])),\n",
     "    'out': tf.Variable(tf.random_normal([n_classes]))\n",