diff --git a/14_recurrent_neural_networks.ipynb b/14_recurrent_neural_networks.ipynb
index 54154c0..ba49bcf 100644
--- a/14_recurrent_neural_networks.ipynb
+++ b/14_recurrent_neural_networks.ipynb
@@ -1427,9 +1427,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
@@ -1440,9 +1438,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n_steps = 28\n",
@@ -1516,9 +1512,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "lstm_cell = tf.contrib.rnn.LSTMCell(num_units=n_neurons, use_peepholes=True)"
@@ -1527,9 +1521,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)"
@@ -1559,9 +1551,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from six.moves import urllib\n",
@@ -1600,9 +1590,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "words = fetch_words_data()"
@@ -1627,9 +1615,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from collections import Counter\n",
@@ -1679,20 +1665,17 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import random\n",
     "from collections import deque\n",
     "\n",
     "def generate_batch(batch_size, num_skips, skip_window):\n",
     "    global data_index\n",
     "    assert batch_size % num_skips == 0\n",
     "    assert num_skips <= 2 * skip_window\n",
-    "    batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
-    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
+    "    batch = np.ndarray(shape=[batch_size], dtype=np.int32)\n",
+    "    labels = np.ndarray(shape=[batch_size, 1], dtype=np.int32)\n",
     "    span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
     "    buffer = deque(maxlen=span)\n",
     "    for _ in range(span):\n",
@@ -1703,7 +1686,7 @@
     "        targets_to_avoid = [ skip_window ]\n",
     "        for j in range(num_skips):\n",
     "            while target in targets_to_avoid:\n",
-    "                target = random.randint(0, span - 1)\n",
+    "                target = np.random.randint(0, span)\n",
     "            targets_to_avoid.append(target)\n",
     "            batch[i * num_skips + j] = buffer[skip_window]\n",
     "            labels[i * num_skips + j, 0] = buffer[target]\n",
@@ -1715,13 +1698,10 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "data_index=0\n",
-    "batch, labels = generate_batch(8, 2, 1)"
+    "np.random.seed(42)"
    ]
   },
   {
@@ -1730,7 +1710,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch, [vocabulary[word] for word in batch]"
+    "data_index = 0\n",
+    "batch, labels = generate_batch(8, 2, 1)"
    ]
   },
   {
@@ -1738,6 +1719,15 @@
    "execution_count": 102,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "batch, [vocabulary[word] for word in batch]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "labels, [vocabulary[word] for word in labels[:, 0]]"
    ]
@@ -1751,10 +1741,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 104,
+   "metadata": {},
    "outputs": [],
    "source": [
     "batch_size = 128\n",
@@ -1775,10 +1763,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 105,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
@@ -1790,10 +1776,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 106,
+   "metadata": {},
    "outputs": [],
    "source": [
     "vocabulary_size = 50000\n",
@@ -1806,10 +1790,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 107,
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_inputs = tf.placeholder(tf.int32, shape=[None])\n",
@@ -1818,10 +1800,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 108,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Construct the variables for the NCE loss\n",
@@ -1842,7 +1822,7 @@
     "training_op = optimizer.minimize(loss)\n",
     "\n",
     "# Compute the cosine similarity between minibatch examples and all embeddings.\n",
-    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keep_dims=True))\n",
+    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keepdims=True))\n",
     "normalized_embeddings = embeddings / norm\n",
     "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
     "similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n",
@@ -1860,7 +1840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1912,10 +1892,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 110,
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.save(\"./my_final_embeddings.npy\", final_embeddings)"
@@ -1930,10 +1908,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 111,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_with_labels(low_dim_embs, labels):\n",
@@ -1952,7 +1928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1981,10 +1957,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 113,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
@@ -2024,10 +1998,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 114,
+   "metadata": {},
    "outputs": [],
    "source": [
     "logits_flat = tf.reshape(logits, [-1, num_decoder_symbols])\n",
@@ -2080,16 +2052,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 115,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from random import choice, seed\n",
-    "\n",
-    "# to make this notebook's output stable across runs\n",
-    "seed(42)\n",
     "np.random.seed(42)\n",
     "\n",
     "default_reber_grammar = [\n",
@@ -2114,7 +2080,8 @@
     "    state = 0\n",
     "    output = []\n",
     "    while state is not None:\n",
-    "        production, state = choice(grammar[state])\n",
+    "        index = np.random.randint(len(grammar[state]))\n",
+    "        production, state = grammar[state][index]\n",
     "        if isinstance(production, list):\n",
     "            production = generate_string(grammar=production)\n",
     "        output.append(production)\n",
@@ -2130,7 +2097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2147,7 +2114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2164,17 +2131,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 118,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def generate_corrupted_string(grammar, chars=\"BEPSTVX\"):\n",
     "    good_string = generate_string(grammar)\n",
     "    index = np.random.randint(len(good_string))\n",
     "    good_char = good_string[index]\n",
-    "    bad_char = choice(list(set(chars) - set(good_char)))\n",
+    "    bad_char = np.random.choice(sorted(set(chars) - set(good_char)))\n",
     "    return good_string[:index] + bad_char + good_string[index + 1:]"
    ]
   },
@@ -2187,7 +2152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2204,10 +2169,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 120,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def string_to_one_hot_vectors(string, n_steps, chars=\"BEPSTVX\"):\n",
@@ -2220,7 +2183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2236,10 +2199,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 122,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def generate_dataset(size):\n",
@@ -2260,10 +2221,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 123,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_train, l_train, y_train = generate_dataset(10000)"
@@ -2278,7 +2237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2294,7 +2253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2310,7 +2269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2328,10 +2287,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 127,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
@@ -2379,10 +2336,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 128,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_val, l_val, y_val = generate_dataset(5000)"
@@ -2390,7 +2345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2423,13 +2378,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
     "test_strings = [\n",
-    "    \"BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVETE\",\n",
-    "    \"BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVEPE\"]\n",
+    "    \"BPBTSSSSSSSXXTTVPXVPXTTTTTVVETE\",\n",
+    "    \"BPBTSSSSSSSXXTTVPXVPXTTTTTVVEPE\"]\n",
     "l_test = np.array([len(s) for s in test_strings])\n",
     "max_length = l_test.max()\n",
     "X_test = [string_to_one_hot_vectors(s, n_steps=max_length)\n",
@@ -2449,7 +2404,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ta-da! It worked fine. The RNN found the correct answers with absolute confidence. :)"
+    "Ta-da! It worked fine. The RNN found the correct answers with high confidence. :)"
    ]
   },
   {
@@ -2469,9 +2424,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -2492,7 +2445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "nav_menu": {},
   "toc": {