diff --git a/Chapter02/cifar10_architecture.json b/Chapter02/cifar10_architecture.json
new file mode 100644
index 0000000..3132fc2
--- /dev/null
+++ b/Chapter02/cifar10_architecture.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "keras_version": "1.1.1", "config": [{"class_name": "Convolution2D", "config": {"b_regularizer": null, "W_constraint": null, "b_constraint": null, "name": "convolution2d_1", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 32, "input_dtype": "float32", "border_mode": "same", "batch_input_shape": [null, 3, 32, 32], "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_1"}}, {"class_name": "Convolution2D", "config": {"W_constraint": null, "b_constraint": null, "name": "convolution2d_2", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 32, "border_mode": "same", "b_regularizer": null, "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_2"}}, {"class_name": "MaxPooling2D", "config": {"name": "maxpooling2d_1", "trainable": true, "dim_ordering": "th", "pool_size": [2, 2], "strides": [2, 2], "border_mode": "valid"}}, {"class_name": "Dropout", "config": {"p": 0.25, "trainable": true, "name": "dropout_1"}}, {"class_name": "Convolution2D", "config": {"W_constraint": null, "b_constraint": null, "name": "convolution2d_3", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 64, "border_mode": "same", "b_regularizer": null, "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_3"}}, {"class_name": "Convolution2D", "config": {"W_constraint": null, "b_constraint": null, "name": "convolution2d_4", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 64, "border_mode": "valid", "b_regularizer": null, "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_4"}}, {"class_name": "MaxPooling2D", "config": {"name": "maxpooling2d_2", "trainable": true, "dim_ordering": "th", "pool_size": [2, 2], "strides": [2, 2], "border_mode": "valid"}}, {"class_name": "Dropout", "config": {"p": 0.25, "trainable": true, "name": "dropout_2"}}, {"class_name": "Flatten", "config": {"trainable": true, "name": "flatten_1"}}, {"class_name": "Dense", "config": {"W_constraint": null, "b_constraint": null, "name": "dense_1", "activity_regularizer": null, "trainable": true, "init": "glorot_uniform", "bias": true, "input_dim": null, "b_regularizer": null, "W_regularizer": null, "activation": "linear", "output_dim": 512}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_5"}}, {"class_name": "Dropout", "config": {"p": 0.5, "trainable": true, "name": "dropout_3"}}, {"class_name": "Dense", "config": {"W_constraint": null, "b_constraint": null, "name": "dense_2", "activity_regularizer": null, "trainable": true, "init": "glorot_uniform", "bias": true, "input_dim": null, "b_regularizer": null, "W_regularizer": null, "activation": "linear", "output_dim": 10}}, {"class_name": "Activation", "config": {"activation": "softmax", "trainable": true, "name": "activation_6"}}]}
\ No newline at end of file
diff --git a/Chapter02/keras_Azure.py b/Chapter02/keras_Azure.py
new file mode 100644
index 0000000..8bc20a9
--- /dev/null
+++ b/Chapter02/keras_Azure.py
@@ -0,0 +1,33 @@
+# The script MUST contain a function named azureml_main
+# which is the entry point for this module.
+
+# imports up here can be used to 
+import pandas as pd
+import theano
+import theano.tensor as T
+from theano import function
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+import numpy as np
+# The entry point function can contain up to two input arguments:
+#   Param<dataframe1>: a pandas.DataFrame
+#   Param<dataframe2>: a pandas.DataFrame
+def azureml_main(dataframe1 = None, dataframe2 = None):
+    # Execution logic goes here
+    # print('Input pandas.DataFrame #1:\r\n\r\n{0}'.format(dataframe1))
+
+    # If a zip file is connected to the third input port is connected,
+    # it is unzipped under ".\Script Bundle". This directory is added
+    # to sys.path. Therefore, if your zip file contains a Python file
+    # mymodule.py you can import it using:
+    # import mymodule
+    model = Sequential()
+    model.add(Dense(1, input_dim=784, activation="relu"))
+    model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
+    data = np.random.random((1000,784))
+    labels = np.random.randint(2, size=(1000,1))
+    model.fit(data, labels, nb_epoch=10, batch_size=32)
+    model.evaluate(data, labels)
+    
+    return dataframe1,
diff --git a/Chapter02/keras_VGG16_prebuilt.py b/Chapter02/keras_VGG16_prebuilt.py
new file mode 100644
index 0000000..4d426ee
--- /dev/null
+++ b/Chapter02/keras_VGG16_prebuilt.py
@@ -0,0 +1,25 @@
+from keras.applications.vgg16 import VGG16
+from keras.models import Model
+from keras.preprocessing import image
+from keras.applications.vgg16 import preprocess_input
+import numpy as np
+
+
+# pre-built and pre-trained deep learning VGG16 model
+base_model = VGG16(weights='imagenet', include_top=True)
+for i, layer in enumerate(base_model.layers):
+	print (i, layer.name, layer.output_shape)
+
+# extract features from block4_pool block
+model = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output)
+
+img_path = 'cat.jpg'
+img = image.load_img(img_path, target_size=(224, 224))
+x = image.img_to_array(img)
+x = np.expand_dims(x, axis=0)
+x = preprocess_input(x)
+
+# get the features from this block
+features = model.predict(x)
+
+print features