diff --git a/seq2seq/attention_decoder.ipynb b/seq2seq/attention_decoder.ipynb
index f4e92d2..dddbcb6 100644
--- a/seq2seq/attention_decoder.ipynb
+++ b/seq2seq/attention_decoder.ipynb
@@ -30,7 +30,39 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "100 2814k  100 2814k    0     0   507k      0  0:00:05  0:00:05 --:--:--  631k\n",
+      "Archive:  data.zip\n",
+      "   creating: data/\n",
+      "  inflating: data/eng-fra.txt        \n",
+      "   creating: data/names/\n",
+      "  inflating: data/names/Arabic.txt   \n",
+      "  inflating: data/names/Chinese.txt  \n",
+      "  inflating: data/names/Czech.txt    \n",
+      "  inflating: data/names/Dutch.txt    \n",
+      "  inflating: data/names/English.txt  \n",
+      "  inflating: data/names/French.txt   \n",
+      "  inflating: data/names/German.txt   \n",
+      "  inflating: data/names/Greek.txt    \n",
+      "  inflating: data/names/Irish.txt    \n",
+      "  inflating: data/names/Italian.txt  \n",
+      "  inflating: data/names/Japanese.txt  \n",
+      "  inflating: data/names/Korean.txt   \n",
+      "  inflating: data/names/Polish.txt   \n",
+      "  inflating: data/names/Portuguese.txt  \n",
+      "  inflating: data/names/Russian.txt  \n",
+      "  inflating: data/names/Scottish.txt  \n",
+      "  inflating: data/names/Spanish.txt  \n",
+      "  inflating: data/names/Vietnamese.txt  \n"
+     ]
+    }
+   ],
    "source": [
     "# download the needed data\n",
     "if not os.path.isfile('data.zip'):\n",
@@ -39,24 +71,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " de question !\n",
+      "Really?\tVraiment ?\n",
+      "Really?\tVrai ?\n",
+      "Really?\tAh bon ?\n",
+      "Thanks.\tMerci !\n",
+      "We try.\tOn essaye.\n",
+      "We won.\tNous avons gagné.\n",
+      "We won.\tNous gagnâmes.\n",
+      "We won.\tNous l'avons emporté.\n",
+      "We won.\tNous l'empor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Take a quick view of the data.\n",
+    "with open('data/eng-fra.txt') as f:\n",
+    "    f.seek(1000)\n",
+    "    print(f.read(200))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
     "class Lang:\n",
+    "    \"\"\"\n",
+    "    Utility class that serves as a language dictionary\n",
+    "    \"\"\"\n",
     "    def __init__(self, name):\n",
     "        self.name = name\n",
+    "        # Count how often a word occurs in the language data.\n",
     "        self.word2count = {}\n",
+    "        # Words are mapped to indices and vice versa\n",
     "        self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
     "        self.word2index = {v:k for k, v in self.index2word.items()}\n",
+    "        # Total word count\n",
     "        self.n_words = 2  # Count SOS and EOS\n",
     "\n",
     "    def add_sentence(self, sentence):\n",
+    "        \"\"\"\n",
+    "        Process words in a sentence string.\n",
+    "        \n",
+    "        :param sentence: (str) \n",
+    "        \"\"\"\n",
     "        for word in sentence.split(' '):\n",
     "            self.add_word(word)\n",
     "\n",
     "    def add_word(self, word):\n",
+    "        \"\"\"\n",
+    "        Process words\n",
+    "        :param word: (str)\n",
+    "        \"\"\"\n",
     "        if word not in self.word2index:\n",
     "            self.word2index[word] = self.n_words\n",
     "            self.word2count[word] = 1\n",
@@ -66,6 +142,9 @@
     "            self.word2count[word] += 1\n",
     "    \n",
     "    def translate_indexes(self, idx):\n",
+    "        \"\"\"\n",
+    "        Takes in a vector of indices and returns the sentence.\n",
+    "        \"\"\"\n",
     "        return [self.index2word[i] for i in idx]\n",
     "    \n",
     "# Turn a Unicode string to plain ASCII, thanks to\n",
@@ -106,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -187,18 +266,23 @@
        "array(['we are even EOS', 'nous sommes a egalite EOS'], dtype='<U60')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def prepare_data(lang1, lang2, reverse=False):\n",
+    "    # read_langs initialized the Lang objects (still empty) and returns the pair sentences.\n",
     "    input_lang, output_lang, pairs = read_langs(lang1, lang2, reverse)\n",
     "    print(\"Read %s sentence pairs\" % len(pairs))\n",
+    "    \n",
+    "    # Reduce data. We haven't got all day to train a model.\n",
     "    pairs = filter_pairs(pairs) \n",
     "    print(\"Trimmed to %s sentence pairs\" % len(pairs))\n",
     "    print(\"Counting words...\")\n",
+    "    \n",
+    "    # Process the language pairs.\n",
     "    for pair in pairs:\n",
     "        input_lang.add_sentence(pair[0])\n",
     "        output_lang.add_sentence(pair[1])\n",
@@ -220,17 +304,32 @@
     "\n",
     "The encoder of a seq2seq network is a RNN that outputs some value for every word from the input sentence. For every input word the encoder outputs a vector and a hidden state, and uses the hidden state for the next input word.\n",
     "\n",
-    "![](img/encoder-network.png)"
+    "![](img/encoder-network.png)\n",
+    "\n",
+    "Every output could be seen as the context of the sentence up to that point.\n",
+    "\n",
+    "![](img/training_seq2seq_many2may.svg)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([5, 1, 2])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "class Encoder(nn.Module):\n",
-    "    def __init__(self, n_words, embedding_size, hidden_size, bidirectional=True, device=device.type):\n",
+    "    def __init__(self, n_words, embedding_size, hidden_size, bidirectional=False, device=device.type):\n",
     "        super(Encoder, self).__init__()\n",
     "        self.bidirectional = bidirectional\n",
     "        self.hidden_size = hidden_size\n",
@@ -241,9 +340,7 @@
     "        self.device = device\n",
     "        if device == 'cuda':\n",
     "            self.cuda()\n",
-    "            \n",
-    "        self.out = nn.Softmax\n",
-    "        \n",
+    "                    \n",
     "    def forward(self, x):\n",
     "        # shape (seq_length, batch_size, input_size)\n",
     "        dense_vector = self.embedding(x).view(x.shape[0], 1, -1)\n",
@@ -258,9 +355,10 @@
     "        return x, h\n",
     "        \n",
     "\n",
-    "m = Encoder(eng.n_words, 10, 2, True, 'cpu')\n",
+    "m = Encoder(eng.n_words, 10, 2, False, 'cpu')\n",
     "scentence = torch.tensor([400, 1, 2, 6, 8])\n",
-    "a = m(scentence)\n"
+    "a = m(scentence)\n",
+    "a[0].shape"
    ]
   },
   {
@@ -273,21 +371,30 @@
     "\n",
     "At every step of decoding, the decoder is given an input token and hidden state. The initial input token is the start-of-string <SOS> token, and the first hidden state is the context vector (the encoder’s last hidden state).\n",
     "    \n",
-    "![](img/decoder-network.png)"
+    "![](img/decoder-network.png)\n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 10])\n",
+      "torch.Size([1, 1, 10])\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "tensor(-23348.0801, grad_fn=<SumBackward0>)"
+       "tensor(-23351.8633, grad_fn=<SumBackward0>)"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,6 +419,11 @@
     "            self.cuda()\n",
     "            \n",
     "    def forward(self, word, h):\n",
+    "        \"\"\"\n",
+    "        :param word: (tensor) Last word or start of sentence token.\n",
+    "        :param h: (tensor) Hidden state or context tensor.\n",
+    "        \"\"\"\n",
+    "        # map from shape (seq_len, embedding_size) to (seq_len, batch, embedding_size) (Notel: seq length is the number of words in the sentence)\n",
     "        word_embedding = self.embedding(word).view(h.shape[0], 1, -1)\n",
     "        a = self.relu(word_embedding)\n",
     "        x, h = self.rnn(a, h)\n",
@@ -357,6 +469,8 @@
     "            nn.Embedding(output_size, embedding_size),\n",
     "            nn.Dropout(dropout)\n",
     "        )\n",
+    "        \n",
+    "        # Seperate neural network to learn the attention weights\n",
     "        self.attention_weights = nn.Sequential(\n",
     "            nn.Linear(embedding_size + hidden_size, max_length),\n",
     "            nn.Softmax(2)\n",
@@ -377,12 +491,14 @@
     "        \n",
     "    def forward(self, word, h, encoder_outputs):\n",
     "        \"\"\"\n",
-    "        :param word: (LongTensor) The word indexes\n",
-    "        :param h: (tensor) The hidden state from the previous step. In the first step, the hidden state of the encoder\n",
-    "        :param encoder_outputs: (tensor) Zero padded (max_length, shape, shape) outputs from the encoder\n",
+    "        :param word: (LongTensor) The word indices. This is the last activated word or \n",
+    "        :param h: (tensor) The hidden state from the previous step. In the first step, the hidden state of the encoder.\n",
+    "        :param encoder_outputs: (tensor) Zero padded (max_length, shape, shape) outputs from the encoder.\n",
     "        \"\"\"\n",
+    "        # map from shape (seq_len, embedding_size) to (seq_len, batch, embedding_size) (Notel: seq length is the number of words in the sentence)\n",
     "        word_embedding = self.embedding(word).view(1, 1, -1)\n",
-    "\n",
+    "        \n",
+    "        # Concatenate the word embedding and the last hidden state, so that attention weights can be determined.\n",
     "        x = torch.cat((word_embedding, h), 2)\n",
     "        attention_weights = self.attention_weights(x)\n",
     "        # attention applied\n",
@@ -849,7 +965,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
diff --git a/seq2seq/img/training_seq2seq_many2may.svg b/seq2seq/img/training_seq2seq_many2may.svg
new file mode 100644
index 0000000..187af75
--- /dev/null
+++ b/seq2seq/img/training_seq2seq_many2may.svg
@@ -0,0 +1,2 @@
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\ No newline at end of file