diff --git a/2.4 Transfer Learning & Fine-Tuning.ipynb b/2.4 Transfer Learning & Fine-Tuning.ipynb
index 7426eb4..9b75068 100644
--- a/2.4 Transfer Learning & Fine-Tuning.ipynb	
+++ b/2.4 Transfer Learning & Fine-Tuning.ipynb	
@@ -43,9 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -106,7 +104,7 @@
    },
    "outputs": [],
    "source": [
-    "if K.image_dim_ordering() == 'th':\n",
+    "if K.image_data_format() == 'channels_first':\n",
     "    input_shape = (1, img_rows, img_cols)\n",
     "else:\n",
     "    input_shape = (img_rows, img_cols, 1)"
@@ -214,9 +212,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -255,9 +251,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -361,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
diff --git a/Extra Additional Materials.ipynb b/Extra Additional Materials.ipynb
index 265d679..a6a6984 100644
--- a/Extra Additional Materials.ipynb	
+++ b/Extra Additional Materials.ipynb	
@@ -8,13 +8,23 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
-   "source": []
+   "source": [
+    "- [Perceptron and Adaline Implementation](./additional materials/1.1.1 Perceptron and Adaline.ipynb)\n",
+    "\n",
+    "- [MLP and MNIST](./additional materials/1.1.2 MLP and MNIST.ipynb)\n",
+    "\n",
+    "- [Quick Theano Tutorial](./additional materials/1.5.1 Introduction - Theano.ipynb)\n",
+    "\n",
+    "- [LSTM for Sentence Generation](./additional materials/3.3 LSTM for Sentence Generation.ipynb)\n",
+    "\n",
+    "- [Custom Layers in Keras](./additional materials/5.1 Custom Layer.ipynb)\n",
+    "\n",
+    "- [Multi-Modal Networks](./additional materials/5.2 Multi-Modal Networks.ipynb)"
+   ]
   }
  ],
  "metadata": {
@@ -33,9 +43,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/additional materials/3.3 LSTM for Sentence Generation.ipynb b/additional materials/3.3 LSTM for Sentence Generation.ipynb
index 9abc183..83aabf6 100644
--- a/additional materials/3.3 LSTM for Sentence Generation.ipynb	
+++ b/additional materials/3.3 LSTM for Sentence Generation.ipynb	
@@ -2,9 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "\n",
     "# RNN using LSTM \n",
@@ -44,9 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from keras.optimizers import SGD\n",
@@ -82,9 +78,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DATA_DIRECTORY = os.path.join(os.path.abspath(os.path.curdir), '..', 'data', 'word_embeddings')\n",
@@ -121,9 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "filtered_male_posts = list(filter(lambda p: len(p) > 0, male_posts))\n",
@@ -133,9 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# text processing - one hot builds index of the words\n",
@@ -158,9 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# 0 for male, 1 for female\n",
@@ -171,9 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.cross_validation import train_test_split\n",
@@ -186,9 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -210,9 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "max_features = 30000\n",
@@ -229,9 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy'])"
@@ -240,9 +220,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -278,9 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -297,9 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -334,9 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "vectorizer = TfidfVectorizer(decode_error='ignore', norm='l2', min_df=5)\n",
@@ -347,9 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "flattened_array_tfidf_male = tfidf_male.toarray()\n",
@@ -359,9 +329,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_rnn = np.concatenate((np.zeros(len(flattened_array_tfidf_male)),\n",
@@ -371,9 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_train_rnn, X_test_rnn, y_train_rnn, y_test_rnn = train_test_split(np.concatenate((flattened_array_tfidf_male, \n",
@@ -384,9 +350,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -436,9 +400,7 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -475,9 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -495,9 +455,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -521,9 +479,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# reading all the male text data into one string\n",
@@ -549,9 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -580,9 +534,7 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -608,9 +560,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "auto_text_generating_male_model.compile(loss='mean_squared_error',optimizer='sgd')"
@@ -649,7 +599,6 @@
    "cell_type": "code",
    "execution_count": 113,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -940,9 +889,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/additional materials/5.2 Multi-Modal Networks.ipynb b/additional materials/5.2 Multi-Modal Networks.ipynb
index 3dbc9e6..f5b04cb 100644
--- a/additional materials/5.2 Multi-Modal Networks.ipynb	
+++ b/additional materials/5.2 Multi-Modal Networks.ipynb	
@@ -4,59 +4,101 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multi-Input Networks"
+    "## Quick Intro to Keras Functional API"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Keras Merge Layer"
+    "## Preamble: All models (layers) are callables"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Multiple Sequential instances can be merged into a single output via a Merge layer. The output is a layer that can be added as first layer in a new Sequential model. For instance, here's a model with two separate input branches getting merged:"
+    "```python\n",
+    "from keras.layers import Input, Dense\n",
+    "from keras.models import Model\n",
+    "\n",
+    "# this returns a tensor\n",
+    "inputs = Input(shape=(784,))\n",
+    "\n",
+    "# a layer instance is callable on a tensor, and returns a tensor\n",
+    "x = Dense(64, activation='relu')(inputs)\n",
+    "x = Dense(64, activation='relu')(x)\n",
+    "predictions = Dense(10, activation='softmax')(x)\n",
+    "\n",
+    "# this creates a model that includes\n",
+    "# the Input layer and three Dense layers\n",
+    "model = Model(input=inputs, output=predictions)\n",
+    "model.compile(optimizer='rmsprop',\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "model.fit(data, labels)  # starts training\n",
+    "```"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Example (from documentation)"
+    "# Multi-Input Networks"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"https://s3.amazonaws.com/keras.io/img/two_branches_sequential_model.png\" >"
+    "## Keras Merge Layer"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a good use case for the functional API: models with multiple inputs and outputs. \n",
+    "\n",
+    "The functional API makes it easy to manipulate a large number of intertwined datastreams.\n",
+    "\n",
+    "Let's consider the following model. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "from keras.layers import Merge, Dense\n",
-    "from keras.models import Sequential\n",
+    "```python\n",
+    "from keras.layers import Dense, Input\n",
+    "from keras.models import Model\n",
+    "from keras.layers.merge import concatenate\n",
     "\n",
-    "left_branch = Sequential()\n",
-    "left_branch.add(Dense(32, input_dim=784))\n",
+    "left_input = Input(shape=(784, ), name='left_input')\n",
+    "left_branch = Dense(32, input_dim=784, name='left_branch')(left_input)\n",
     "\n",
-    "right_branch = Sequential()\n",
-    "right_branch.add(Dense(32, input_dim=784))\n",
+    "right_input = Input(shape=(784,), name='right_input')\n",
+    "right_branch = Dense(32, input_dim=784, name='right_branch')(right_input)\n",
     "\n",
-    "merged = Merge([left_branch, right_branch], mode='concat')\n",
+    "x = concatenate([left_branch, right_branch])\n",
+    "predictions = Dense(10, activation='softmax', name='main_output')(x)\n",
     "\n",
-    "final_model = Sequential()\n",
-    "final_model.add(merged)\n",
-    "final_model.add(Dense(10, activation='softmax'))"
+    "model = Model(inputs=[left_input, right_input], outputs=predictions)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resulting Model will look like the following network:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../imgs/multi_input_model.png\" />"
    ]
   },
   {
@@ -71,8 +113,8 @@
    "metadata": {},
    "source": [
     "```python\n",
-    "final_model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
-    "final_model.fit([input_data_1, input_data_2], targets)  # we pass one data array per model input\n",
+    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "model.fit([input_data_1, input_data_2], targets)  # we pass one data array per model input\n",
     "```"
    ]
   },
@@ -80,44 +122,82 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Merge layer supports a number of pre-defined modes:\n",
-    "\n",
-    "* `sum` (default): element-wise sum\n",
-    "* `concat`: tensor concatenation. You can specify the concatenation axis via the argument concat_axis.\n",
-    "* `mul`: element-wise multiplication\n",
-    "* `ave`: tensor average\n",
-    "* `dot`: dot product. You can specify which axes to reduce along via the argument dot_axes.\n",
-    "* `cos`: cosine proximity between vectors in 2D tensors."
+    "## Try yourself"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also pass a function as the mode argument, allowing for arbitrary transformations:"
+    "#### Step 1: Get Data - MNIST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# let's load MNIST data as we did in the exercise on MNIST with FC Nets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %load ../solutions/sol_52.py"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```python\n",
-    "merged = Merge([left_branch, right_branch], mode=lambda x: x[0] - x[1])\n",
-    "```"
+    "#### Step 2: Create the Multi-Input Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## try yourself\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## `evaluate` the model on test data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Keras Functional API"
+    "Keras supports different Merge strategies:\n",
+    "\n",
+    "* `add`: element-wise sum\n",
+    "* `concatenate`: tensor concatenation. You can specify the concatenation axis via the argument concat_axis.\n",
+    "* `multiply`: element-wise multiplication\n",
+    "* `average`: tensor average\n",
+    "* `maximum`: element-wise maximum of the inputs.\n",
+    "* `dot`: dot product. You can specify which axes to reduce along via the argument dot_axes. You can also specify applying any normalisation. In that case, the output of the dot product is the cosine proximity between the two samples."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### All models (layers) are callables"
+    "You can also pass a function as the mode argument, allowing for arbitrary transformations:"
    ]
   },
   {
@@ -125,24 +205,7 @@
    "metadata": {},
    "source": [
     "```python\n",
-    "from keras.layers import Input, Dense\n",
-    "from keras.models import Model\n",
-    "\n",
-    "# this returns a tensor\n",
-    "inputs = Input(shape=(784,))\n",
-    "\n",
-    "# a layer instance is callable on a tensor, and returns a tensor\n",
-    "x = Dense(64, activation='relu')(inputs)\n",
-    "x = Dense(64, activation='relu')(x)\n",
-    "predictions = Dense(10, activation='softmax')(x)\n",
-    "\n",
-    "# this creates a model that includes\n",
-    "# the Input layer and three Dense layers\n",
-    "model = Model(input=inputs, output=predictions)\n",
-    "model.compile(optimizer='rmsprop',\n",
-    "              loss='categorical_crossentropy',\n",
-    "              metrics=['accuracy'])\n",
-    "model.fit(data, labels)  # starts training\n",
+    "merged = Merge([left_branch, right_branch], mode=lambda x: x[0] - x[1])\n",
     "```"
    ]
   }
diff --git a/imgs/multi_input_model.png b/imgs/multi_input_model.png
new file mode 100644
index 0000000..7c00498
Binary files /dev/null and b/imgs/multi_input_model.png differ
diff --git a/solutions/sol_52.py b/solutions/sol_52.py
new file mode 100644
index 0000000..49995df
--- /dev/null
+++ b/solutions/sol_52.py
@@ -0,0 +1,26 @@
+from keras.datasets import mnist
+from keras.utils import np_utils
+
+img_rows, img_cols = 28, 28
+nb_classes = 10
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+    
+X_train = X_train.reshape(X_train.shape[0], img_rows*img_cols)
+X_test = X_test.reshape(X_test.shape[0], img_rows*img_cols)
+
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+X_train /= 255
+X_test /= 255
+
+print('X_train shape:', X_train.shape)
+print('y_train shape:', y_train.shape)
+print('X_test shape:', X_test.shape)
+print('y_test shape:', y_test.shape)
+print(X_train.shape[0], 'train samples')
+print(X_test.shape[0], 'test samples')
+
+y_train = np_utils.to_categorical(y_train, nb_classes)
+y_test = np_utils.to_categorical(y_test, nb_classes)
\ No newline at end of file