diff --git a/README.md b/README.md
index f3c4c50..a997345 100644
--- a/README.md
+++ b/README.md
@@ -12,8 +12,8 @@
 
 ## Index
 
-* [scikit-learn](#scikit-learn)
 * [kaggle-and-business-analyses](#kaggle-and-business-analyses)
+* [scikit-learn](#scikit-learn)
 * [deep-learning](#deep-learning)
 * [statistical-inference-scipy](#statistical-inference-scipy)
 * [pandas](#pandas)
@@ -31,6 +31,20 @@
 * [contact-info](#contact-info)
 * [license](#license)
 
+<br/>
+<p align="center">
+  <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
+</p>
+
+## kaggle-and-business-analyses
+
+IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
+
+| Notebook | Description |
+|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
+| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predict survival on the Titanic.  Learn data cleaning, exploratory data analysis, and machine learning. |
+| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predict customer churn.  Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors.  Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
+
 <br/>
 <p align="center">
   <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png">
@@ -52,20 +66,6 @@ IPython Notebook(s) demonstrating scikit-learn functionality.
 | [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Implement Gaussian mixture models in scikit-learn. |
 | [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Implement validation and model selection in scikit-learn. |
 
-<br/>
-<p align="center">
-  <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
-</p>
-
-## kaggle-and-business-analyses
-
-IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
-
-| Notebook | Description |
-|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
-| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predict survival on the Titanic.  Learn data cleaning, exploratory data analysis, and machine learning. |
-| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predict customer churn.  Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors.  Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
-
 <br/>
 <p align="center">
   <img src="http://i.imgur.com/ZhKXrKZ.png">
@@ -97,6 +97,8 @@ Additional TensorFlow tutorials:
 
 * [pkmital/tensorflow_tutorials](https://github.com/pkmital/tensorflow_tutorials)
 * [nlintz/TensorFlow-Tutorials](https://github.com/nlintz/TensorFlow-Tutorials)
+* [alrojo/tensorflow-tutorial](https://github.com/alrojo/tensorflow-tutorial)
+* [BinRoot/TensorFlow-Book](https://github.com/BinRoot/TensorFlow-Book)
 
 | Notebook | Description |
 |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -324,11 +326,13 @@ For detailed instructions, scripts, and tools to set up your development environ
 
 To view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the ipython notebook.  More information on IPython Notebooks can be found [here.](http://ipython.org/notebook.html)
 
-```
-$ git clone https://github.com/donnemartin/data-science-ipython-notebooks.git
-$ cd data-science-ipython-notebooks
-$ ipython notebook
-```
+    $ git clone https://github.com/donnemartin/data-science-ipython-notebooks.git
+    $ cd data-science-ipython-notebooks
+    $ ipython notebook
+
+If you have Jupyter Notebook 4+, run the following instead of `ipython notebook`:
+
+    $ jupyter notebook
 
 Notebooks tested with Python 2.7.x.
 
@@ -341,6 +345,8 @@ Notebooks tested with Python 2.7.x.
 * [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien
 * [TensorFlow Tutorials](https://github.com/pkmital/tensorflow_tutorials) by Parag K Mital
 * [TensorFlow Tutorials](https://github.com/nlintz/TensorFlow-Tutorials) by Nathan Lintz
+* [TensorFlow Tutorials](https://github.com/alrojo/tensorflow-tutorial) by Alexander R Johansen
+* [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) by Nishant Shukla
 * [Summer School 2015](https://github.com/mila-udem/summerschool2015) by mila-udem
 * [Kaggle](https://www.kaggle.com/)
 * [Yhat Blog](http://blog.yhat.com/)
diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
index 2e28a0c..8d70ee2 100644
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
@@ -230,7 +230,7 @@
    "outputs": [],
    "source": [
     "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
    ]
   },
   {
@@ -337,7 +337,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.5+"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb
index 967550a..38b4cda 100644
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb
@@ -213,7 +213,7 @@
    "outputs": [],
    "source": [
     "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
    ]
   },
   {
@@ -313,7 +313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.5+"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb
index f925827..692c694 100644
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb
@@ -165,7 +165,7 @@
    "outputs": [],
    "source": [
     "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
    ]
   },
   {
@@ -231,21 +231,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb
index 41e3e1f..dfe9e74 100644
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb
@@ -236,7 +236,7 @@
    ],
    "source": [
     "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()\n",
+    "init = tf.global_variables_initializer()\n",
     "\n",
     "# Launch the graph\n",
     "with tf.Session() as sess:\n",
@@ -286,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.5+"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb b/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb
index df2e803..e3d7dc7 100644
--- a/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb
+++ b/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb
@@ -381,7 +381,7 @@
     "  # This is a one-time operation which ensures the parameters get initialized as\n",
     "  # we described in the graph: random weights for the matrix, zeros for the\n",
     "  # biases. \n",
-    "  tf.initialize_all_variables().run()\n",
+    " tf.global_variables_initializer().run()\n",
     "  print 'Initialized'\n",
     "  for step in xrange(num_steps):\n",
     "    # Run the computations. We tell .run() that we want to run the optimizer,\n",
@@ -544,7 +544,7 @@
     "num_steps = 3001\n",
     "\n",
     "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
     "  print \"Initialized\"\n",
     "  for step in xrange(num_steps):\n",
     "    # Pick an offset within the training data, which has been randomized.\n",
@@ -589,21 +589,21 @@
   "colab_default_view": {},
   "colab_views": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/deep-learning/tensor-flow-exercises/4_convolutions.ipynb b/deep-learning/tensor-flow-exercises/4_convolutions.ipynb
index 5062c0b..be60f3a 100644
--- a/deep-learning/tensor-flow-exercises/4_convolutions.ipynb
+++ b/deep-learning/tensor-flow-exercises/4_convolutions.ipynb
@@ -412,7 +412,7 @@
     "num_steps = 1001\n",
     "\n",
     "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
     "  print \"Initialized\"\n",
     "  for step in xrange(num_steps):\n",
     "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
@@ -467,21 +467,21 @@
   "colab_default_view": {},
   "colab_views": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/deep-learning/tensor-flow-exercises/5_word2vec.ipynb b/deep-learning/tensor-flow-exercises/5_word2vec.ipynb
index 31257ac..5407fdf 100644
--- a/deep-learning/tensor-flow-exercises/5_word2vec.ipynb
+++ b/deep-learning/tensor-flow-exercises/5_word2vec.ipynb
@@ -744,7 +744,7 @@
     "num_steps = 100001\n",
     "\n",
     "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
     "  print \"Initialized\"\n",
     "  average_loss = 0\n",
     "  for step in xrange(num_steps):\n",
@@ -867,21 +867,21 @@
   "colab_default_view": {},
   "colab_views": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/deep-learning/tensor-flow-exercises/6_lstm.ipynb b/deep-learning/tensor-flow-exercises/6_lstm.ipynb
index 4417bd2..565bee5 100644
--- a/deep-learning/tensor-flow-exercises/6_lstm.ipynb
+++ b/deep-learning/tensor-flow-exercises/6_lstm.ipynb
@@ -928,7 +928,7 @@
     "summary_frequency = 100\n",
     "\n",
     "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
     "  print 'Initialized'\n",
     "  mean_loss = 0\n",
     "  for step in xrange(num_steps):\n",
@@ -1042,21 +1042,21 @@
   "colab_default_view": {},
   "colab_views": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,