Clean up deep learning and SciPy sections.

Standardized notebook names, added SciPy section header.

Author: donnemartin

README.md

@@ -87,13 +87,13 @@ 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. |
+| [tsf-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. |
+| [tsf-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. |
+| [tsf-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. |
+| [tsf-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. |
+| [tsf-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. |
+| [tsf-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. |
 
 <br/>
 <p align="center">
@@ -123,10 +123,12 @@ IPython Notebook(s) demonstrating scikit-learn functionality.
 
 ## statistical-inference-scipy
 
+IPython Notebook(s) demonstrating statistical inference with SciPy functionality.
+
 | Notebook | Description |
 |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | scipy | SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. |
-| [effect_size](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/effect_size.ipynb) | Effect size. |
+| [effect-size](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/effect_size.ipynb) | Effect size. |
 | [sampling](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/sampling.ipynb) | Random sampling. |
 | [hypothesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/hypothesis.ipynb) | Hypothesis testing. |