diff --git a/README.md b/README.md
index 62db081..935ccee 100644
--- a/README.md
+++ b/README.md
@@ -18,15 +18,15 @@ For detailed instructions, scripts, and tools to more optimally set up your deve
* [spark](#spark)
* [mapreduce-python](#mapreduce-python)
-* [amazon web services](#aws)
* [kaggle-and-business-analyses](#kaggle-and-business-analyses)
-* [scikit-learn](#scikit-learn)
* [deep-learning](#deep-learning)
+* [scikit-learn](#scikit-learn)
* [statistical-inference-scipy](#statistical-inference-scipy)
* [pandas](#pandas)
* [matplotlib](#matplotlib)
* [numpy](#numpy)
* [python-data](#python-data)
+* [amazon web services](#aws)
* [command lines](#commands)
* [misc](#misc)
* [notebook-installation](#notebook-installation)
@@ -62,30 +62,6 @@ IPython Notebook(s) demonstrating Hadoop MapReduce with mrjob functionality.
|--------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|
| [mapreduce-python](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/mapreduce/mapreduce-python.ipynb) | Supports MapReduce jobs in Python with [mrjob](https://github.com/Yelp/mrjob), running them locally or on Hadoop clusters. Demonstrates mrjob code, unit test, and config file to analyze Amazon S3 bucket logs on Elastic MapReduce. [Disco](https://github.com/discoproject/disco/) is another python-based alternative.|
-
-
- 
-
-
-## aws
-
-IPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality.
-
-Also check out:
-
-* [SAWS](https://github.com/donnemartin/saws): A Supercharged AWS command line interface (CLI).
-* [Awesome AWS](https://github.com/donnemartin/awesome-aws): A curated list of libraries, open source repos, guides, blogs, and other resources.
-
-| Notebook | Description |
-|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [boto](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#Boto) | Official AWS SDK for Python. |
-| [s3cmd](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3cmd) | Interacts with S3 through the command line. |
-| [s3distcp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3distcp) | Combines smaller files and aggregates them together by taking in a pattern and target file. S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster. |
-| [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put) | Uploads multiple files to S3 in parallel. |
-| [redshift](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#redshift) | Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP). |
-| [kinesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#kinesis) | Streams data in real time with the ability to process thousands of data streams per second. |
-| [lambda](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#lambda) | Runs code in response to events, automatically managing compute resources. |
-
@@ -100,6 +76,25 @@ IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and b
| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predicts survival on the Titanic. Demonstrates 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) | Predicts customer churn. Exercises logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Discussion of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
+
+
+ 
+
+
+## deep-learning
+
+IPython Notebook(s) demonstrating deep learning functionality.
+
+| Notebook | Description |
+|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [ts-not-mnist](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/1_notmnist.ipynb) | Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow. |
+| [ts-fully-connected](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb) | Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow. |
+| [ts-regularization](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/3_regularization.ipynb) | Explore regularization techniques by training fully connected networks to classify notMNIST characters in TensorFlow. |
+| [ts-convolutions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/4_convolutions.ipynb) | Create convolutional neural networks in TensorFlow. |
+| [ts-word2vec](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/5_word2vec.ipynb) | Train a skip-gram model over Text8 data in TensorFlow. |
+| [ts-lstm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/6_lstm.ipynb) | Train a LSTM character model over Text8 data in TensorFlow. |
+| [deep dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |
+
@@ -121,25 +116,6 @@ 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) | Gaussian mixture models. |
| [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Validation and model selection. |
-
-
-
-
-
-## deep-learning
-
-IPython Notebook(s) demonstrating deep learning functionality.
-
-| Notebook | Description |
-|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [deep dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |
-| [ts-not-mnist](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/1_notmnist.ipynb) | Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow. |
-| [ts-fully-connected](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb) | Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow. |
-| [ts-regularization](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/3_regularization.ipynb) | Explore regularization techniques by training fully connected networks to classify notMNIST characters in TensorFlow. |
-| [ts-convolutions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/4_convolutions.ipynb) | Create convolutional neural networks in TensorFlow. |
-| [ts-word2vec](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/5_word2vec.ipynb) | Train a skip-gram model over Text8 data in TensorFlow. |
-| [ts-lstm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/6_lstm.ipynb) | Train a LSTM character model over Text8 data in TensorFlow. |
-
@@ -213,6 +189,30 @@ IPython Notebook(s) demonstrating Python functionality geared towards data analy
| [pdb](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/pdb.ipynb) | Interactive source code debugger. |
| [unit tests](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/unit_tests.ipynb) | Nose unit tests. |
+
+
+
+
+
+## aws
+
+IPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality.
+
+Also check out:
+
+* [SAWS](https://github.com/donnemartin/saws): A Supercharged AWS command line interface (CLI).
+* [Awesome AWS](https://github.com/donnemartin/awesome-aws): A curated list of libraries, open source repos, guides, blogs, and other resources.
+
+| Notebook | Description |
+|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [boto](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#Boto) | Official AWS SDK for Python. |
+| [s3cmd](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3cmd) | Interacts with S3 through the command line. |
+| [s3distcp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3distcp) | Combines smaller files and aggregates them together by taking in a pattern and target file. S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster. |
+| [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put) | Uploads multiple files to S3 in parallel. |
+| [redshift](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#redshift) | Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP). |
+| [kinesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#kinesis) | Streams data in real time with the ability to process thousands of data streams per second. |
+| [lambda](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#lambda) | Runs code in response to events, automatically managing compute resources. |
+
