all code examples should be up now

Author: rasbt

README.md

@@ -44,7 +44,7 @@ Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/prefac
 10. Predicting Continuous Target Variables with Regression Analysis [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
 11. Working with Unlabeled Data – Clustering Analysis [[dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
 12. Training Artificial Neural Networks for Image Recognition [[dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
-13. Parallelizing Neural Network Training via Theano
+13. Parallelizing Neural Network Training via Theano [[dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
 
 
 ### [Literature References & Further Reading Resources](./docs/references.md)

code/_convenience_scripts/blank_tocs/ch04.toc

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+Dealing with missing data
+  Eliminating samples or features with missing values
+  Imputing missing values
+  Understanding the scikit-learn estimator API
+Handling categorical data
+  Mapping ordinal features
+  Encoding class labels
+  Performing one-hot encoding on nominal features
+Partitioning a dataset in training and test sets
+Bringing features onto the same scale
+Selecting meaningful features
+  Sparse solutions with L1 regularization
+  Sequential feature selection algorithms
+Assessing feature importance with random forests
+Summary

code/_convenience_scripts/blank_tocs/ch05.toc

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+Unsupervised dimensionality reduction via principal component analysis 128
+  Total and explained variance
+  Feature transformation
+  Principal component analysis in scikit-learn
+Supervised data compression via linear discriminant analysis
+  Computing the scatter matrices
+  Selecting linear discriminants for the new feature subspace
+  Projecting samples onto the new feature space
+  LDA via scikit-learn
+Using kernel principal component analysis for nonlinear mappings
+  Kernel functions and the kernel trick
+  Implementing a kernel principal component analysis in Python
+    Example 1 – separating half-moon shapes
+    Example 2 – separating concentric circles
+  Projecting new data points
+  Kernel principal component analysis in scikit-learn
+Summary

code/_convenience_scripts/blank_tocs/ch06.toc

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+Streamlining workflows with pipelines  Loading the Breast Cancer Wisconsin dataset  Combining transformers and estimators in a pipelineUsing k-fold cross-validation to assess model performance  The holdout method  K-fold cross-validationDebugging algorithms with learning and validation curves  Diagnosing bias and variance problems with learning curves  Addressing overfitting and underfitting with validation curvesFine-tuning machine learning models via grid search  Tuning hyperparameters via grid search  Algorithm selection with nested cross-validationLooking at different performance evaluation metrics  Reading a confusion matrix
+  Optimizing the precision and recall of a classification model
+  Plotting a receiver operating characteristic  The scoring metrics for multiclass classification
+Summary

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+Learning with ensembles
+Implementing a simple majority vote classifier
+  Combining different algorithms for classification with majority vote
+Evaluating and tuning the ensemble classifierBagging – building an ensemble of classifiers from bootstrap samples
+Leveraging weak learners via adaptive boosting
+Summary

code/_convenience_scripts/blank_tocs/ch08.toc

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+Obtaining the IMDb movie review dataset
+Introducing the bag-of-words model  Transforming words into feature vectors
+  Assessing word relevancy via term frequency-inverse document frequency 
+  Cleaning text data
+  Processing documents into tokensTraining a logistic regression model for document classification
+Working with bigger data – online algorithms and out-of-core learning
+Summary

code/_convenience_scripts/blank_tocs/ch09.toc

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+Serializing fitted scikit-learn estimators
+Setting up a SQLite database for data storage 
+Developing a web application with Flask
+Our first Flask web application
+  Form validation and rendering
+  Turning the movie classifier into a web applicationDeploying the web application to a public server  Updating the movie review classifierSummary

code/_convenience_scripts/blank_tocs/ch10.toc

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+Introducing a simple linear regression model
+Exploring the Housing Dataset
+  Visualizing the important characteristics of a dataset
+Implementing an ordinary least squares linear regression model
+  Solving regression for regression parameters with gradient descent
+  Estimating the coefficient of a regression model via scikit-learn
+Fitting a robust regression model using RANSAC
+Evaluating the performance of linear regression models
+Using regularized methods for regressionTurning a linear regression model into a curve – polynomial regression
+  Modeling nonlinear relationships in the Housing Dataset  Dealing with nonlinear relationships using random forests
+    Decision tree regression
+    Random forest regressionSummary

code/_convenience_scripts/blank_tocs/ch11.toc

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+Grouping objects by similarity using k-means
+  K-means++
+  Hard versus soft clustering
+  Using the elbow method to find the optimal number of clusters
+  Quantifying the quality of clustering via silhouette plots
+Organizing clusters as a hierarchical tree
+  Performing hierarchical clustering on a distance matrix
+  Attaching dendrograms to a heat map
+  Applying agglomerative clustering via scikit-learn
+Locating regions of high density via DBSCAN
+Summary

code/_convenience_scripts/blank_tocs/ch12.toc

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+Modeling complex functions with artificial neural networks
+  Single-layer neural network recap
+  Introducing the multi-layer neural network architecture
+  Activating a neural network via forward propagation
+Classifying handwritten digits  Obtaining the MNIST dataset  Implementing a multi-layer perceptronTraining an artificial neural network  Computing the logistic cost function  Training neural networks via backpropagationDeveloping your intuition for backpropagation
+Debugging neural networks with gradient checking 
+Convergence in neural networks 
+Other neural network architectures  Convolutional Neural Networks  Recurrent Neural NetworksA few last words about neural network implementation
+Summary