diff --git a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
index d983b0f4153c11b174fbed6eccab0ce2a0b12db6..c31c5f9ec41b98faec67a01be4dfbd9090c463cb 100644
--- a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
+++ b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
@@ -7,6 +7,13 @@
     "# Train a Quantized MLP on UNSW-NB15 with Brevitas"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font color=\"red\">**FPGA'21 tutorial:** We recommend clicking **Cell -> Run All** when you start reading this notebook for \"latency hiding\".</font>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -41,8 +48,6 @@
     "\n",
     "* [Initial setup](#initial_setup)\n",
     "* [Load the UNSW_NB15 dataset](#load_dataset) \n",
-    "    * [(Option 1, slower) Download original and quantize](#dataset_qnt_manual)\n",
-    "    * [(Option 2, faster) Use prequantized version](#dataset_qnt_pre)\n",
     "* [Define the Quantized MLP model](#define_quantized_mlp)\n",
     "* [Define Train and Test  Methods](#train_test)\n",
     "    * [(Option 1) Train the Model from Scratch](#train_scratch)\n",
@@ -76,17 +81,13 @@
     "\n",
     "### Dataset Quantization <a id='dataset_qnt'></a>\n",
     "\n",
-    "The goal of this notebook is to train a Quantized Neural Network (QNN) to be later deployed as an FPGA accelerator generated by the FINN compiler. Although we can choose a variety of different precisions for the input, [Murovic and Trost](https://ev.fe.uni-lj.si/1-2-2019/Murovic.pdf) have previously shown we can actually binarize the inputs and still get good (90%+) accuracy.\n",
-    "\n",
-    "For this part we offer two options: you may choose to download the original dataset and apply the quantization functions on it (slower), or use a pre-quantized version (faster). Both options will yield the same result, though the first option gives more details on how we quantize the dataset."
+    "The goal of this notebook is to train a Quantized Neural Network (QNN) to be later deployed as an FPGA accelerator generated by the FINN compiler. Although we can choose a variety of different precisions for the input, [Murovic and Trost](https://ev.fe.uni-lj.si/1-2-2019/Murovic.pdf) have previously shown we can actually binarize the inputs and still get good (90%+) accuracy."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Download original and quantize <a id='dataset_qnt_manual'></a>\n",
-    "\n",
     "We will create a binarized representation for the dataset by following the procedure defined by [Murovic and Trost](https://ev.fe.uni-lj.si/1-2-2019/Murovic.pdf), which we repeat briefly here:\n",
     "\n",
     "* Original features have different formats ranging from integers, floating numbers to strings.\n",
@@ -98,34 +99,16 @@
     "\n",
     "Following Murovic and Trost's open-source implementation provided as a Matlab script [here](https://github.com/TadejMurovic/BNN_Deployment/blob/master/cybersecurity_dataset_unswb15.m), we've created a [Python version](dataloader_quantized.py).\n",
     "\n",
-    "However, downloading the original dataset and quantizing it can take some time, so we provide a pre-quantized version for your convenience. Uncomment the following line to download:"
+    "<font color=\"red\">**FPGA'21 tutorial:** Downloading the original dataset and quantizing it can take some time, so we provide a pre-quantized version for your convenience. The prequantized dataset (unsw_nb15_binarized.npz) should already be available on the AWS instance. If not, uncomment the following line to download:</font>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2021-02-18 09:24:29--  https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1\n",
-      "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
-      "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 13391907 (13M) [application/octet-stream]\n",
-      "Saving to: 'unsw_nb15_binarized.npz'\n",
-      "\n",
-      "unsw_nb15_binarized 100%[===================>]  12.77M  6.12MB/s    in 2.1s    \n",
-      "\n",
-      "2021-02-18 09:24:32 (6.12 MB/s) - 'unsw_nb15_binarized.npz' saved [13391907/13391907]\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "! wget -O unsw_nb15_binarized.npz https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1"
+    "# ! wget -O unsw_nb15_binarized.npz https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1"
    ]
   },
   {
@@ -137,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -183,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -236,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,13 +241,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "from brevitas.nn import QuantLinear, QuantReLU\n",
     "import torch.nn as nn\n",
     "\n",
+    "# Setting seeds for reproducibility\n",
+    "torch.manual_seed(0)\n",
+    "\n",
     "model = nn.Sequential(\n",
     "      QuantLinear(input_size, hidden1, bias=True, weight_bit_width=weight_bit_width),\n",
     "      nn.BatchNorm1d(hidden1),\n",
@@ -299,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,11 +367,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_epochs = 15\n",
+    "num_epochs = 10\n",
     "lr = 0.001 \n",
     "\n",
     "def display_loss_plot(losses, title=\"Training loss\", xlabel=\"Iterations\", ylabel=\"Loss\"):\n",
@@ -399,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -419,7 +405,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Training loss = 0.128492 test accuracy = 0.792705: 100%|██████████| 15/15 [04:28<00:00, 17.97s/it]\n"
+      "Training loss = 0.132480 test accuracy = 0.797989: 100%|██████████| 10/10 [00:57<00:00,  5.74s/it]\n"
      ]
     }
    ],
@@ -447,14 +433,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -475,12 +461,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -498,16 +484,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7927051450225915"
+       "0.7979886313948404"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -518,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -546,7 +532,7 @@
        "IncompatibleKeys(missing_keys=[], unexpected_keys=[])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -572,7 +558,7 @@
        "0.9188772287810328"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -599,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -608,7 +594,7 @@
        "(64, 593)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -624,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -633,7 +619,7 @@
        "(64, 600)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -648,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -657,7 +643,7 @@
        "torch.Size([64, 600])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -680,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -708,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -738,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -747,7 +733,7 @@
        "0.9188772287810328"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -767,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "scrolled": true
    },
@@ -815,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -839,10 +825,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f06a9bcd0f0>"
+       "<IPython.lib.display.IFrame at 0x7fa9a3c044e0>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }