diff --git a/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb b/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
index 9103388de..1e11dc1a8 100644
--- a/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
+++ b/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -573,8 +573,138 @@
      "text": [
       "Episode: 20, Average reward for episode 23.000000.\n",
       "Episode: 40, Average reward for episode 22.300000.\n",
-      "Episode: 60, Average reward for episode 22.950000.\n"
+      "Episode: 60, Average reward for episode 22.950000.\n",
+      "Episode: 80, Average reward for episode 17.600000.\n",
+      "Episode: 100, Average reward for episode 17.900000.\n",
+      "Episode: 120, Average reward for episode 14.500000.\n",
+      "Episode: 140, Average reward for episode 15.000000.\n",
+      "Episode: 160, Average reward for episode 17.600000.\n",
+      "Episode: 180, Average reward for episode 14.100000.\n",
+      "Episode: 200, Average reward for episode 15.250000.\n",
+      "Episode: 220, Average reward for episode 14.350000.\n",
+      "Episode: 240, Average reward for episode 13.600000.\n",
+      "Episode: 260, Average reward for episode 14.550000.\n",
+      "Episode: 280, Average reward for episode 12.300000.\n",
+      "Episode: 300, Average reward for episode 13.250000.\n",
+      "Episode: 320, Average reward for episode 12.850000.\n",
+      "Episode: 340, Average reward for episode 13.950000.\n",
+      "Episode: 360, Average reward for episode 14.650000.\n",
+      "Episode: 380, Average reward for episode 13.350000.\n",
+      "Episode: 400, Average reward for episode 14.300000.\n",
+      "Episode: 420, Average reward for episode 13.350000.\n",
+      "Episode: 440, Average reward for episode 14.700000.\n",
+      "Episode: 460, Average reward for episode 12.850000.\n",
+      "Episode: 480, Average reward for episode 13.500000.\n",
+      "Episode: 500, Average reward for episode 12.650000.\n",
+      "Episode: 520, Average reward for episode 13.150000.\n",
+      "Episode: 540, Average reward for episode 13.900000.\n",
+      "Episode: 560, Average reward for episode 13.350000.\n",
+      "Episode: 580, Average reward for episode 11.500000.\n",
+      "Episode: 600, Average reward for episode 13.800000.\n",
+      "Episode: 620, Average reward for episode 11.150000.\n",
+      "Episode: 640, Average reward for episode 12.750000.\n",
+      "Episode: 660, Average reward for episode 12.150000.\n",
+      "Episode: 680, Average reward for episode 11.950000.\n",
+      "Episode: 700, Average reward for episode 12.000000.\n",
+      "Episode: 720, Average reward for episode 12.900000.\n",
+      "Episode: 740, Average reward for episode 11.700000.\n",
+      "Episode: 760, Average reward for episode 11.000000.\n",
+      "Episode: 780, Average reward for episode 11.050000.\n",
+      "Episode: 800, Average reward for episode 11.150000.\n",
+      "Episode: 820, Average reward for episode 11.650000.\n",
+      "Episode: 840, Average reward for episode 13.200000.\n",
+      "Episode: 860, Average reward for episode 12.100000.\n",
+      "Episode: 880, Average reward for episode 12.750000.\n",
+      "Episode: 900, Average reward for episode 12.100000.\n",
+      "Episode: 920, Average reward for episode 13.750000.\n",
+      "Episode: 940, Average reward for episode 14.150000.\n",
+      "Episode: 960, Average reward for episode 14.950000.\n",
+      "Episode: 980, Average reward for episode 15.750000.\n",
+      "Episode: 1000, Average reward for episode 16.500000.\n",
+      "Episode: 1020, Average reward for episode 17.650000.\n",
+      "Episode: 1040, Average reward for episode 19.600000.\n",
+      "Episode: 1060, Average reward for episode 21.600000.\n",
+      "Episode: 1080, Average reward for episode 28.350000.\n",
+      "Episode: 1100, Average reward for episode 19.300000.\n",
+      "Episode: 1120, Average reward for episode 10.300000.\n",
+      "Episode: 1140, Average reward for episode 10.850000.\n",
+      "Episode: 1160, Average reward for episode 10.450000.\n",
+      "Episode: 1180, Average reward for episode 10.800000.\n",
+      "Episode: 1200, Average reward for episode 10.350000.\n",
+      "Episode: 1220, Average reward for episode 24.400000.\n",
+      "Episode: 1240, Average reward for episode 49.100000.\n",
+      "Task solved in 1240 episodes\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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DAIyK36SrrVnd2wEAgmO+0pVbNuAjhCxdqyuzNKQzANv2R7uZy3RlNo0OBgAMXr5BV25B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\niIiIiKiY/T+eY29O5x6F9wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c0c71cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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u0JwZ3zYEgONj6USdNTymdWM0GD9PVyZxfCQAx8a3ppP0nSh2+6heAIBl+47q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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c13d8160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ac2GBfpi6aZeuTHiDmgCAaT/v1pXrW78GIlZv0ZX5unk9AEDr6ct05Vb2aQ8A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\n44/Stvbs2QM/Pz9kZmaalp05cwZOTk7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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c13d8128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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bryvXv7E/Rn2/TVfmm3aNAQCdF67RlVv2VXsA+raxf2N/AEDjKfrqftuI7Lqf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c26e03c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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LdWXiJva1uy3AvsfZ3jHHnnHA3jFn8MpvdeXmfByMkPBvdGWW9W4PAPh631Fd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\nCdi01gmQXfdaa6VMmTJWB71qdQIAhw8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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c2764d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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mJuL48eMQQmDDhg1wcHDQdecTHjknIiIiIiKiF9Kfva95p06dNM3XokUL7N27F507d0bx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\nrl0zTktKSjK5AJuShIQE1KpVS3W+fC4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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c0f30240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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x48bw8vLCxx9/jPPnz+tq48X5MwIREb1Udu/end+LQERERC+5vDhynpGRgUGDBiE+Pl5x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\n+RvZVoa+6DcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c2cd73c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -661,11 +791,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2017-02-02 17:49:05,100] Making new env: CartPole-v0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
    "source": [
     "import cntk as C\n",
     "env = gym.make('CartPole-v0')\n",
@@ -716,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -734,11 +888,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x133c28cce10>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+2pNPPmlNnTrV8nq9juZobW21rrzySuvo0aP22N13322tXr3awVRnVVRUWNHR0VZ9fb09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\nJQoMAOBKFBgAwJX+H5605FuM4BHAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c2874ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "discounted_epr = discount_rewards(np.ones(10))\n",
     "f, ax = plt.subplots(1, figsize=(5,2))\n",
@@ -754,11 +929,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x133c2e7dc88>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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11104fPgw7rrrrl75kCHrQ5qBgYFwdnZGeXm5eay0tBRBQUESpro95OTkoLCw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\nWHhERCQLLDwiIpKF/wM8lOd+C0pLlwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c2c8dd30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "discounted_epr_cent = discounted_epr - np.mean(discounted_epr)\n",
     "discounted_epr_norm = discounted_epr_cent/np.std(discounted_epr_cent)\n",
@@ -768,11 +964,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x133ca402358>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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44IMP5I4kunz5MhITExEaGorIyEjs3r27x44taaeVP6Jb9++rq6vDokWL4OPjg4kTJ8od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\nEf4/mBiZv9YJ6xYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133c35c9c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "discounted_epr = discount_rewards(np.ones(10), gamma=0.5)\n",
     "discounted_epr_cent = discounted_epr - np.mean(discounted_epr)\n",
@@ -838,7 +1055,52 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Episode: 20. Average reward for episode 19.500000.\n",
+      "Episode: 40. Average reward for episode 19.150000.\n",
+      "Episode: 60. Average reward for episode 16.150000.\n",
+      "Episode: 80. Average reward for episode 15.500000.\n",
+      "Episode: 100. Average reward for episode 19.500000.\n",
+      "Episode: 120. Average reward for episode 19.850000.\n",
+      "Episode: 140. Average reward for episode 17.000000.\n",
+      "Episode: 160. Average reward for episode 18.150000.\n",
+      "Episode: 180. Average reward for episode 19.650000.\n",
+      "Episode: 200. Average reward for episode 21.500000.\n",
+      "Episode: 220. Average reward for episode 16.850000.\n",
+      "Episode: 240. Average reward for episode 17.850000.\n",
+      "Episode: 260. Average reward for episode 21.450000.\n",
+      "Episode: 280. Average reward for episode 16.800000.\n",
+      "Episode: 300. Average reward for episode 17.700000.\n",
+      "Episode: 320. Average reward for episode 17.800000.\n",
+      "Episode: 340. Average reward for episode 17.850000.\n",
+      "Episode: 360. Average reward for episode 19.450000.\n",
+      "Episode: 380. Average reward for episode 20.400000.\n",
+      "Episode: 400. Average reward for episode 19.650000.\n",
+      "Episode: 420. Average reward for episode 22.950000.\n",
+      "Episode: 440. Average reward for episode 21.250000.\n",
+      "Episode: 460. Average reward for episode 18.450000.\n",
+      "Episode: 480. Average reward for episode 16.850000.\n",
+      "Episode: 500. Average reward for episode 16.800000.\n",
+      "Episode: 520. Average reward for episode 19.150000.\n",
+      "Episode: 540. Average reward for episode 18.400000.\n",
+      "Episode: 560. Average reward for episode 22.150000.\n",
+      "Episode: 580. Average reward for episode 18.500000.\n",
+      "Episode: 600. Average reward for episode 17.750000.\n",
+      "Episode: 620. Average reward for episode 19.950000.\n",
+      "Episode: 640. Average reward for episode 19.150000.\n",
+      "Episode: 660. Average reward for episode 19.650000.\n",
+      "Episode: 680. Average reward for episode 20.850000.\n",
+      "Episode: 700. Average reward for episode 20.000000.\n",
+      "Episode: 720. Average reward for episode 16.500000.\n",
+      "Episode: 740. Average reward for episode 22.350000.\n",
+      "Episode: 760. Average reward for episode 24.250000.\n"
+     ]
+    }
+   ],
    "source": [
     "input_y = C.input_variable(1, np.float32, name=\"input_y\")\n",
     "advantages = C.input_variable(1, np.float32, name=\"advt\")\n",
diff --git a/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb b/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
index 85dd91fd0..4046fed3d 100644
--- a/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
+++ b/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
@@ -1,5 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -11,20 +22,78 @@
     "\n",
     "This hands-on tutorial will take you through both the basics of sequence-to-sequence networks, and how to implement them in the Microsoft Cognitive Toolkit. In particular, we will implement a sequence-to-sequence model to perform grapheme to phoneme translation. We will start with some basic theory and then explain the data in more detail, and how you can download it.\n",
     "\n",
-    "Andrej Karpathy has a [nice visualization](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) of the five paradigms of neural network architectures:\n",
-    "\n",
-    "<img src=http://cntk.ai/jup/paradigms.jpg width=750px>\n",
-    "\n",
+    "Andrej Karpathy has a [nice visualization](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) of the five paradigms of neural network architectures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"http://cntk.ai/jup/paradigms.jpg\" width=\"750\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Figure 1\n",
+    "Image(url=\"http://cntk.ai/jup/paradigms.jpg\", width=750)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "In this tutorial, we are going to be talking about the fourth paradigm: many-to-many, also known as sequence-to-sequence networks. The input is a sequence with a dynamic length, and the output is also a sequence with some dynamic length. It is the logical extension of the many-to-one paradigm in that previously we were predicting some category (which could easily be one of `V` words where `V` is an entire vocabulary) and now we want to predict a whole sequence of those categories.\n",
     "\n",
     "The applications of sequence-to-sequence networks are nearly limitless. It is a natural fit for machine translation (e.g. English input sequences, French output sequences); automatic text summarization (e.g. full document input sequence, summary output sequence); word to pronunciation models (e.g. character [grapheme] input sequence, pronunciation [phoneme] output sequence); and even parse tree generation (e.g. regular text input, flat parse tree output).\n",
     "\n",
     "## Basic theory\n",
     "\n",
-    "A sequence-to-sequence model consists of two main pieces: (1) an encoder; and (2) a decoder. Both the encoder and the decoder are recurrent neural network (RNN) layers that can be implemented using a vanilla RNN, an LSTM, or GRU cells (here we will use LSTM). In the basic sequence-to-sequence model, the encoder processes the input sequence into a fixed representation that is fed into the decoder as a context. The decoder then uses some mechanism (discussed below) to decode the processed information into an output sequence. The decoder is a language model that is augmented with some \"strong context\" by the encoder, and so each symbol that it generates is fed back into the decoder for additional context (like a traditional LM). For an English to German translation task, the most basic setup might look something like this:\n",
-    "\n",
-    "<img src=http://cntk.ai/jup/s2s.png width=700px>\n",
-    "\n",
+    "A sequence-to-sequence model consists of two main pieces: (1) an encoder; and (2) a decoder. Both the encoder and the decoder are recurrent neural network (RNN) layers that can be implemented using a vanilla RNN, an LSTM, or GRU cells (here we will use LSTM). In the basic sequence-to-sequence model, the encoder processes the input sequence into a fixed representation that is fed into the decoder as a context. The decoder then uses some mechanism (discussed below) to decode the processed information into an output sequence. The decoder is a language model that is augmented with some \"strong context\" by the encoder, and so each symbol that it generates is fed back into the decoder for additional context (like a traditional LM). For an English to German translation task, the most basic setup might look something like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"http://cntk.ai/jup/s2s.png\" width=\"700\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Figure 2\n",
+    "Image(url=\"http://cntk.ai/jup/s2s.png\", width=700)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "The basic sequence-to-sequence network passes the information from the encoder to the decoder by initializing the decoder RNN with the final hidden state of the encoder as its initial hidden state. The input is then a \"sequence start\" tag (`<s>` in the diagram above) which primes the decoder to start generating an output sequence. Then, whatever word (or note or image, etc.) it generates at that step is fed in as the input for the next step. The decoder keeps generating outputs until it hits the special \"end sequence\" tag (`</s>` above).\n",
     "\n",
     "A more complex and powerful version of the basic sequence-to-sequence network uses an attention model. While the above setup works well, it can start to break down when the input sequences get long. At each step, the hidden state `h` is getting updated with the most recent information, and therefore `h` might be getting \"diluted\" in information as it processes each token. Further, even with a relatively short sequence, the last token will always get the last say and therefore the thought vector will be somewhat biased/weighted towards that last word. To deal with this problem, we use an \"attention\" mechanism that allows the decoder to look not only at all of the hidden states from the input, but it also learns which hidden states, for each step in decoding, to put the most weight on. We will discuss an attention implementation in a later version of this tutorial."
@@ -87,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -137,11 +206,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reusing locally cached:  ..\\Examples\\SequenceToSequence\\CMUDict\\Data\\tiny.ctf\n",
+      "Reusing locally cached:  ..\\Examples\\SequenceToSequence\\CMUDict\\Data\\cmudict-0.7b.train-dev-20-21.ctf\n",
+      "Reusing locally cached:  ..\\Examples\\SequenceToSequence\\CMUDict\\Data\\cmudict-0.7b.mapping\n"
+     ]
+    }
+   ],
    "source": [
     "import requests\n",
     "\n",
@@ -187,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "collapsed": true
    },
@@ -207,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -230,11 +309,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocabulary size is 69\n",
+      "First 15 letters are:\n",
+      "[\"'\", '</s>', '<s/>', '<s>', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']\n",
+      "\n",
+      "Print dictionary with the vocabulary mapping:\n",
+      "{0: \"'\", 1: '</s>', 2: '<s/>', 3: '<s>', 4: 'A', 5: 'B', 6: 'C', 7: 'D', 8: 'E', 9: 'F', 10: 'G', 11: 'H', 12: 'I', 13: 'J', 14: 'K', 15: 'L', 16: 'M', 17: 'N', 18: 'O', 19: 'P', 20: 'Q', 21: 'R', 22: 'S', 23: 'T', 24: 'U', 25: 'V', 26: 'W', 27: 'X', 28: 'Y', 29: 'Z', 30: '~AA', 31: '~AE', 32: '~AH', 33: '~AO', 34: '~AW', 35: '~AY', 36: '~B', 37: '~CH', 38: '~D', 39: '~DH', 40: '~EH', 41: '~ER', 42: '~EY', 43: '~F', 44: '~G', 45: '~HH', 46: '~IH', 47: '~IY', 48: '~JH', 49: '~K', 50: '~L', 51: '~M', 52: '~N', 53: '~NG', 54: '~OW', 55: '~OY', 56: '~P', 57: '~R', 58: '~S', 59: '~SH', 60: '~T', 61: '~TH', 62: '~UH', 63: '~UW', 64: '~V', 65: '~W', 66: '~Y', 67: '~Z', 68: '~ZH'}\n"
+     ]
+    }
+   ],
    "source": [
     "# Print vocab and the correspoding mapping to the phonemes\n",
     "print(\"Vocabulary size is\", len(vocab))\n",
@@ -254,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -289,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -325,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -363,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -404,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
@@ -454,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "collapsed": true
    },
@@ -500,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -522,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -549,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "collapsed": true
    },
@@ -599,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "collapsed": true
    },
@@ -624,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "collapsed": true
    },
@@ -713,11 +805,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['raw_labels', 'raw_input']\n"
+     ]
+    }
+   ],
    "source": [
     "model = create_model()\n",
     "label_sequence = model.find_by_name('label_sequence')\n",
@@ -739,7 +839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -767,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -794,11 +894,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minibatch: 0, Train Loss: 4.234, Train Evaluation Criterion: 1.000\n"
+     ]
+    }
+   ],
    "source": [
     "training_progress_output_freq = 100\n",
     "max_num_minibatch = 100 if isFast else 1000\n",
@@ -827,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "collapsed": true
    },
@@ -846,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "collapsed": true
    },
@@ -881,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -984,11 +1092,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minibatch: 0, Train Loss: 4.234, Train Evaluation Criterion: 1.000\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "['</s>', '</s>', '</s>', '</s>', 'C', '</s>']\n",
+      "--- EPOCH 0 DONE: loss = 3.817505, errs = 87.036621 ---\n"
+     ]
+    }
+   ],
    "source": [
     "# Given a vocab and tensor, print the output\n",
     "def print_sequences(sequences, i2w):\n",
@@ -1009,11 +1136,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "87.03662098404853\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the training error\n",
     "print(error)"
@@ -1051,7 +1186,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1065,7 +1200,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.5"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb b/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
index 640eeb920..efe7e31fe 100644
--- a/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
+++ b/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
@@ -493,7 +493,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/Tutorials/CNTK_206_Basic_GAN.ipynb b/Tutorials/CNTK_206_Basic_GAN.ipynb
index 781432cf8..268f3c5f4 100644
--- a/Tutorials/CNTK_206_Basic_GAN.ipynb
+++ b/Tutorials/CNTK_206_Basic_GAN.ipynb
@@ -1,5 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -17,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -56,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -86,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": true
    },
@@ -105,11 +116,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data directory is ..\\Examples\\Image\\DataSets\\MNIST\n"
+     ]
+    }
+   ],
    "source": [
     "# Ensure the training data is generated and available for this tutorial\n",
     "# We search in two locations in the toolkit for the cached MNIST data set.\n",
@@ -130,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -161,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
@@ -184,10 +203,39 @@
     "\n",
     "A GAN network is composed of two sub-networks, one called the Generator ($G$) and the other Discriminator ($D$). \n",
     "- The **Generator** takes random noise vector ($z$) as input and strives to output synthetic (fake) image ($x^*$) that is indistinguishable from the real image ($x$) from the MNIST dataset. \n",
-    "- The **Discriminator** strives to differentiate between the real image ($x$) and the fake ($x^*$) image.\n",
-    "\n",
-    "<img src = \"https://www.cntk.ai/jup/GAN_basic_flow.png\">\n",
-    "\n",
+    "- The **Discriminator** strives to differentiate between the real image ($x$) and the fake ($x^*$) image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://www.cntk.ai/jup/GAN_basic_flow.png\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Figure 1\n",
+    "Image(url=\"https://www.cntk.ai/jup/GAN_basic_flow.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "In each training iteration, the Generator produces more realistic fake images (in other words *minimizes* the difference between the real and generated counterpart) and also the Discriminator *maximizes* the probability of assigning the correct label (real vs. fake) to both real examples (from training set) and the generated fake ones. The two conflicting objectives between the sub-networks ($G$ and $D$) leads to the GAN network (when trained) converge to an equilibrium, where the Generator produces realistic looking fake MNIST images and the Discriminator can at best randomly guess whether images are real or fake. The resulting Generator model once trained produces realistic MNIST image with the input being a random number. "
    ]
   },
@@ -217,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
@@ -233,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -247,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "collapsed": true
    },
@@ -270,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
@@ -302,15 +350,38 @@
     "    \\min_G \\max_D V(D,G)= \\mathbb{E}_{x}[ log D(x) ] + \\mathbb{E}_{z}[ log(1 - D(G(z))) ]\n",
     "$$\n",
     "\n",
-    "At the optimal point of this game the generator will produce realistic looking data while the discriminator will predict that the generated image is indeed fake with a probability of 0.5. The [algorithm referred below](https://arxiv.org/pdf/1406.2661v1.pdf) is implemented in this tutorial.\n",
-    "\n",
-    "\n",
-    "<img src = \"https://www.cntk.ai/jup/GAN_goodfellow_NIPS2014.png\", width = 500>\n"
+    "At the optimal point of this game the generator will produce realistic looking data while the discriminator will predict that the generated image is indeed fake with a probability of 0.5. The [algorithm referred below](https://arxiv.org/pdf/1406.2661v1.pdf) is implemented in this tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://www.cntk.ai/jup/GAN_goodfellow_NIPS2014.png\" width=\"500\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Figure 2\n",
+    "Image(url=\"https://www.cntk.ai/jup/GAN_goodfellow_NIPS2014.png\", width = 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -370,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -414,12 +485,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Minibatch[   1-   6]: loss = 2.737928 * 6144;\n",
+      " Minibatch[   1-   6]: loss = 0.327561 * 6144;\n",
+      " Minibatch[   7-  12]: loss = 2.355194 * 6144;\n",
+      " Minibatch[   7-  12]: loss = 0.438016 * 6144;\n",
+      " Minibatch[  13-  18]: loss = 2.486605 * 6144;\n",
+      " Minibatch[  13-  18]: loss = 0.381973 * 6144;\n",
+      " Minibatch[  19-  24]: loss = 2.645467 * 6144;\n",
+      " Minibatch[  19-  24]: loss = 0.356883 * 6144;\n",
+      " Minibatch[  25-  30]: loss = 2.468790 * 6144;\n",
+      " Minibatch[  25-  30]: loss = 0.708103 * 6144;\n",
+      " Minibatch[  31-  36]: loss = 1.954979 * 6144;\n",
+      " Minibatch[  31-  36]: loss = 1.153848 * 6144;\n",
+      " Minibatch[  37-  42]: loss = 1.939908 * 6144;\n",
+      " Minibatch[  37-  42]: loss = 0.921016 * 6144;\n",
+      " Minibatch[  43-  48]: loss = 1.815944 * 6144;\n",
+      " Minibatch[  43-  48]: loss = 1.392506 * 6144;\n",
+      " Minibatch[  49-  54]: loss = 1.872496 * 6144;\n",
+      " Minibatch[  49-  54]: loss = 0.842418 * 6144;\n",
+      " Minibatch[  55-  60]: loss = 1.709652 * 6144;\n",
+      " Minibatch[  55-  60]: loss = 1.135109 * 6144;\n",
+      " Minibatch[  61-  66]: loss = 1.671173 * 6144;\n",
+      " Minibatch[  61-  66]: loss = 1.007829 * 6144;\n",
+      " Minibatch[  67-  72]: loss = 1.803653 * 6144;\n",
+      " Minibatch[  67-  72]: loss = 1.039823 * 6144;\n",
+      " Minibatch[  73-  78]: loss = 1.854205 * 6144;\n",
+      " Minibatch[  73-  78]: loss = 1.112219 * 6144;\n",
+      " Minibatch[  79-  84]: loss = 1.599514 * 6144;\n",
+      " Minibatch[  79-  84]: loss = 0.979602 * 6144;\n",
+      " Minibatch[  85-  90]: loss = 1.554925 * 6144;\n",
+      " Minibatch[  85-  90]: loss = 1.067822 * 6144;\n",
+      " Minibatch[  91-  96]: loss = 1.602542 * 6144;\n",
+      " Minibatch[  91-  96]: loss = 1.084749 * 6144;\n",
+      " Minibatch[  97- 102]: loss = 1.490171 * 6144;\n",
+      " Minibatch[  97- 102]: loss = 1.108200 * 6144;\n",
+      " Minibatch[ 103- 108]: loss = 1.523889 * 6144;\n",
+      " Minibatch[ 103- 108]: loss = 1.091449 * 6144;\n",
+      " Minibatch[ 109- 114]: loss = 1.945248 * 6144;\n",
+      " Minibatch[ 109- 114]: loss = 1.096080 * 6144;\n",
+      " Minibatch[ 115- 120]: loss = 1.686950 * 6144;\n",
+      " Minibatch[ 115- 120]: loss = 0.983363 * 6144;\n",
+      " Minibatch[ 121- 126]: loss = 1.748998 * 6144;\n",
+      " Minibatch[ 121- 126]: loss = 0.944750 * 6144;\n",
+      " Minibatch[ 127- 132]: loss = 1.829200 * 6144;\n",
+      " Minibatch[ 127- 132]: loss = 0.886693 * 6144;\n",
+      " Minibatch[ 133- 138]: loss = 1.876721 * 6144;\n",
+      " Minibatch[ 133- 138]: loss = 0.768756 * 6144;\n",
+      " Minibatch[ 139- 144]: loss = 1.946413 * 6144;\n",
+      " Minibatch[ 139- 144]: loss = 0.721226 * 6144;\n",
+      " Minibatch[ 145- 150]: loss = 2.188425 * 6144;\n",
+      " Minibatch[ 145- 150]: loss = 0.554943 * 6144;\n",
+      " Minibatch[ 151- 156]: loss = 2.261010 * 6144;\n",
+      " Minibatch[ 151- 156]: loss = 0.521321 * 6144;\n",
+      " Minibatch[ 157- 162]: loss = 2.284071 * 6144;\n",
+      " Minibatch[ 157- 162]: loss = 0.538548 * 6144;\n",
+      " Minibatch[ 163- 168]: loss = 2.188945 * 6144;\n",
+      " Minibatch[ 163- 168]: loss = 0.577333 * 6144;\n",
+      " Minibatch[ 169- 174]: loss = 2.079304 * 6144;\n",
+      " Minibatch[ 169- 174]: loss = 0.701605 * 6144;\n",
+      " Minibatch[ 175- 180]: loss = 2.175129 * 6144;\n",
+      " Minibatch[ 175- 180]: loss = 0.644715 * 6144;\n",
+      " Minibatch[ 181- 186]: loss = 2.011760 * 6144;\n",
+      " Minibatch[ 181- 186]: loss = 0.723366 * 6144;\n",
+      " Minibatch[ 187- 192]: loss = 2.116358 * 6144;\n",
+      " Minibatch[ 187- 192]: loss = 0.712513 * 6144;\n",
+      " Minibatch[ 193- 198]: loss = 2.175821 * 6144;\n",
+      " Minibatch[ 193- 198]: loss = 0.641268 * 6144;\n",
+      " Minibatch[ 199- 204]: loss = 2.105059 * 6144;\n",
+      " Minibatch[ 199- 204]: loss = 0.677874 * 6144;\n",
+      " Minibatch[ 205- 210]: loss = 1.966301 * 6144;\n",
+      " Minibatch[ 205- 210]: loss = 0.741405 * 6144;\n",
+      " Minibatch[ 211- 216]: loss = 1.969168 * 6144;\n",
+      " Minibatch[ 211- 216]: loss = 0.792235 * 6144;\n",
+      " Minibatch[ 217- 222]: loss = 1.796313 * 6144;\n",
+      " Minibatch[ 217- 222]: loss = 0.852187 * 6144;\n",
+      " Minibatch[ 223- 228]: loss = 1.910731 * 6144;\n",
+      " Minibatch[ 223- 228]: loss = 0.819637 * 6144;\n",
+      " Minibatch[ 229- 234]: loss = 1.777931 * 6144;\n",
+      " Minibatch[ 229- 234]: loss = 0.874954 * 6144;\n",
+      " Minibatch[ 235- 240]: loss = 1.988461 * 6144;\n",
+      " Minibatch[ 235- 240]: loss = 0.794293 * 6144;\n",
+      " Minibatch[ 241- 246]: loss = 1.963283 * 6144;\n",
+      " Minibatch[ 241- 246]: loss = 0.777755 * 6144;\n",
+      " Minibatch[ 247- 252]: loss = 1.849503 * 6144;\n",
+      " Minibatch[ 247- 252]: loss = 0.823517 * 6144;\n",
+      " Minibatch[ 253- 258]: loss = 1.983176 * 6144;\n",
+      " Minibatch[ 253- 258]: loss = 0.850665 * 6144;\n",
+      " Minibatch[ 259- 264]: loss = 2.031011 * 6144;\n",
+      " Minibatch[ 259- 264]: loss = 0.826843 * 6144;\n",
+      " Minibatch[ 265- 270]: loss = 1.972308 * 6144;\n",
+      " Minibatch[ 265- 270]: loss = 0.787416 * 6144;\n",
+      " Minibatch[ 271- 276]: loss = 1.929749 * 6144;\n",
+      " Minibatch[ 271- 276]: loss = 0.801013 * 6144;\n",
+      " Minibatch[ 277- 282]: loss = 2.026559 * 6144;\n",
+      " Minibatch[ 277- 282]: loss = 0.771508 * 6144;\n",
+      " Minibatch[ 283- 288]: loss = 2.040789 * 6144;\n",
+      " Minibatch[ 283- 288]: loss = 0.713442 * 6144;\n",
+      " Minibatch[ 289- 294]: loss = 2.022175 * 6144;\n",
+      " Minibatch[ 289- 294]: loss = 0.706637 * 6144;\n",
+      " Minibatch[ 295- 300]: loss = 2.034424 * 6144;\n",
+      " Minibatch[ 295- 300]: loss = 0.755113 * 6144;\n"
+     ]
+    }
+   ],
    "source": [
     "reader_train = create_reader(train_file, True, d_input_dim, label_dim=10)\n",
     "\n",
@@ -428,11 +606,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss of the generator is: 2.07\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the generator loss \n",
     "print(\"Training loss of the generator is: {0:.2f}\".format(G_trainer_loss))"
@@ -449,12 +635,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wgPn5eVZWVvac/25V+23NewM8ePCAdDpt5BMfvx1u5Y//+I85ceIES0tLzMzMbIti7Idm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\nfD8QCHD27FkmJiaYmprahyt8PmgnRLvdjqIoZLNZarUaDoeDUqmE0Wj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HaDSK0+kUk48a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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f204ed81d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def plot_images(images, subplot_shape):\n",
     "    plt.style.use('ggplot')\n",
@@ -473,10 +670,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Larger number of iterations should generate more realistic looking MNIST images. A sampling of such generated images are shown below.\n",
-    "\n",
-    "<img src=\"http://www.cntk.ai/jup/GAN_basic_slowmode.jpg\">\n",
-    "\n",
+    "Larger number of iterations should generate more realistic looking MNIST images. A sampling of such generated images are shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"http://www.cntk.ai/jup/GAN_basic_slowmode.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Figure 3\n",
+    "Image(url=\"http://www.cntk.ai/jup/GAN_basic_slowmode.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "**Note**: It takes a large number of iterations to capture a representation of the real world signal. Even simple dense networks can be quite effective in modelling data albeit MNIST is a relatively simple dataset as well."
    ]
   },
@@ -510,7 +736,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -524,7 +750,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,