diff --git a/docs/equations/pymle-equations.pdf b/docs/equations/pymle-equations.pdf
index f3d1c472..d341c6da 100644
Binary files a/docs/equations/pymle-equations.pdf and b/docs/equations/pymle-equations.pdf differ
diff --git a/docs/equations/pymle-equations.tex b/docs/equations/pymle-equations.tex
index 0668d40d..3527646c 100644
--- a/docs/equations/pymle-equations.tex
+++ b/docs/equations/pymle-equations.tex
@@ -1203,6 +1203,9 @@ \subsection{K-fold cross-validation}
 \section{Debugging algorithms with learning and validation curves}
 \subsection{Diagnosing bias and variance problems with learning curves}
 \subsection{Addressing overfitting and underfitting with validation curves}
+
+\newpage
+
 \section{Fine-tuning machine learning models via grid search}
 \subsection{Tuning hyperparameters via grid search}
 \subsection{Algorithm selection with nested cross-validation}
diff --git a/docs/equations/pymle-equations.toc b/docs/equations/pymle-equations.toc
index 8b052553..8023f0ed 100644
--- a/docs/equations/pymle-equations.toc
+++ b/docs/equations/pymle-equations.toc
@@ -81,15 +81,15 @@
 \contentsline {subsection}{\numberline {5.3.4}Kernel principal component analysis in scikit-learn}{36}{subsection.5.3.4}
 \contentsline {section}{\numberline {5.4}Summary}{36}{section.5.4}
 \contentsline {chapter}{\numberline {6}Learning Best Practices for Model Evaluation and Hyperparameter Tuning}{37}{chapter.6}
-\contentsline {section}{\numberline {6.1}Streamlining workflows with pipelines}{38}{section.6.1}
-\contentsline {subsection}{\numberline {6.1.1}Loading the Breast Cancer Wisconsin dataset}{38}{subsection.6.1.1}
-\contentsline {subsection}{\numberline {6.1.2}Combining transformers and estimators in a pipeline}{38}{subsection.6.1.2}
-\contentsline {section}{\numberline {6.2}Using k-fold cross-validation to assess model performance}{38}{section.6.2}
-\contentsline {subsection}{\numberline {6.2.1}The holdout method}{38}{subsection.6.2.1}
-\contentsline {subsection}{\numberline {6.2.2}K-fold cross-validation}{38}{subsection.6.2.2}
-\contentsline {section}{\numberline {6.3}Debugging algorithms with learning and validation curves}{38}{section.6.3}
-\contentsline {subsection}{\numberline {6.3.1}Diagnosing bias and variance problems with learning curves}{38}{subsection.6.3.1}
-\contentsline {subsection}{\numberline {6.3.2}Addressing overfitting and underfitting with validation curves}{38}{subsection.6.3.2}
+\contentsline {section}{\numberline {6.1}Streamlining workflows with pipelines}{37}{section.6.1}
+\contentsline {subsection}{\numberline {6.1.1}Loading the Breast Cancer Wisconsin dataset}{37}{subsection.6.1.1}
+\contentsline {subsection}{\numberline {6.1.2}Combining transformers and estimators in a pipeline}{37}{subsection.6.1.2}
+\contentsline {section}{\numberline {6.2}Using k-fold cross-validation to assess model performance}{37}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}The holdout method}{37}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}K-fold cross-validation}{37}{subsection.6.2.2}
+\contentsline {section}{\numberline {6.3}Debugging algorithms with learning and validation curves}{37}{section.6.3}
+\contentsline {subsection}{\numberline {6.3.1}Diagnosing bias and variance problems with learning curves}{37}{subsection.6.3.1}
+\contentsline {subsection}{\numberline {6.3.2}Addressing overfitting and underfitting with validation curves}{37}{subsection.6.3.2}
 \contentsline {section}{\numberline {6.4}Fine-tuning machine learning models via grid search}{38}{section.6.4}
 \contentsline {subsection}{\numberline {6.4.1}Tuning hyperparameters via grid search}{38}{subsection.6.4.1}
 \contentsline {subsection}{\numberline {6.4.2}Algorithm selection with nested cross-validation}{38}{subsection.6.4.2}
@@ -98,92 +98,92 @@
 \contentsline {subsection}{\numberline {6.5.2}Optimizing the precision and recall of a classification model}{38}{subsection.6.5.2}
 \contentsline {subsection}{\numberline {6.5.3}Plotting a receiver operating characteristic}{39}{subsection.6.5.3}
 \contentsline {subsection}{\numberline {6.5.4}The scoring metrics for multiclass classification}{39}{subsection.6.5.4}
-\contentsline {section}{\numberline {6.6}Summary}{40}{section.6.6}
-\contentsline {chapter}{\numberline {7}Combining Different Models for Ensemble Learning}{41}{chapter.7}
-\contentsline {section}{\numberline {7.1}Learning with ensembles}{41}{section.7.1}
-\contentsline {section}{\numberline {7.2}Implementing a simple majority vote classifier}{42}{section.7.2}
-\contentsline {subsection}{\numberline {7.2.1}Combining different algorithms for classification with majority vote}{43}{subsection.7.2.1}
-\contentsline {section}{\numberline {7.3}Evaluating and tuning the ensemble classifier}{43}{section.7.3}
-\contentsline {section}{\numberline {7.4}Bagging -- building an ensemble of classifiers from bootstrap samples}{43}{section.7.4}
-\contentsline {section}{\numberline {7.5}Leveraging weak learners via adaptive boosting}{43}{section.7.5}
-\contentsline {section}{\numberline {7.6}Summary}{45}{section.7.6}
-\contentsline {chapter}{\numberline {8}Applying Machine Learning to Sentiment Analysis}{46}{chapter.8}
-\contentsline {section}{\numberline {8.1}Obtaining the IMDb movie review dataset}{46}{section.8.1}
-\contentsline {section}{\numberline {8.2}Introducing the bag-of-words model}{46}{section.8.2}
-\contentsline {subsection}{\numberline {8.2.1}Transforming words into feature vectors}{46}{subsection.8.2.1}
-\contentsline {subsection}{\numberline {8.2.2}Assessing word relevancy via term frequency-inverse document frequency}{46}{subsection.8.2.2}
-\contentsline {subsection}{\numberline {8.2.3}Cleaning text data}{47}{subsection.8.2.3}
-\contentsline {subsection}{\numberline {8.2.4}Processing documents into tokens}{47}{subsection.8.2.4}
-\contentsline {section}{\numberline {8.3}Training a logistic regression model for document classification}{47}{section.8.3}
-\contentsline {section}{\numberline {8.4}Working with bigger data - online algorithms and out-of-core learning}{47}{section.8.4}
-\contentsline {section}{\numberline {8.5}Summary}{47}{section.8.5}
-\contentsline {chapter}{\numberline {9}Embedding a Machine Learning Model into a Web Application}{48}{chapter.9}
-\contentsline {section}{\numberline {9.1}Chapter 8 recap - Training a model for movie review classification}{48}{section.9.1}
-\contentsline {section}{\numberline {9.2}Serializing fitted scikit-learn estimators}{48}{section.9.2}
-\contentsline {section}{\numberline {9.3}Setting up a SQLite database for data storage Developing a web application with Flask}{48}{section.9.3}
-\contentsline {section}{\numberline {9.4}Our first Flask web application}{48}{section.9.4}
-\contentsline {subsection}{\numberline {9.4.1}Form validation and rendering}{48}{subsection.9.4.1}
-\contentsline {subsection}{\numberline {9.4.2}Turning the movie classifier into a web application}{48}{subsection.9.4.2}
-\contentsline {section}{\numberline {9.5}Deploying the web application to a public server}{48}{section.9.5}
-\contentsline {subsection}{\numberline {9.5.1}Updating the movie review classifier}{48}{subsection.9.5.1}
-\contentsline {section}{\numberline {9.6}Summary}{48}{section.9.6}
-\contentsline {chapter}{\numberline {10}Predicting Continuous Target Variables with Regression Analysis}{49}{chapter.10}
-\contentsline {section}{\numberline {10.1}Introducing a simple linear regression model}{49}{section.10.1}
-\contentsline {section}{\numberline {10.2}Exploring the Housing Dataset}{49}{section.10.2}
-\contentsline {subsection}{\numberline {10.2.1}Visualizing the important characteristics of a dataset}{49}{subsection.10.2.1}
-\contentsline {section}{\numberline {10.3}Implementing an ordinary least squares linear regression model}{51}{section.10.3}
-\contentsline {subsection}{\numberline {10.3.1}Solving regression for regression parameters with gradient descent}{51}{subsection.10.3.1}
-\contentsline {subsection}{\numberline {10.3.2}Estimating the coefficient of a regression model via scikit-learn}{51}{subsection.10.3.2}
-\contentsline {section}{\numberline {10.4}Fitting a robust regression model using RANSAC}{51}{section.10.4}
-\contentsline {section}{\numberline {10.5}Evaluating the performance of linear regression models}{51}{section.10.5}
-\contentsline {section}{\numberline {10.6}Using regularized methods for regression}{52}{section.10.6}
-\contentsline {section}{\numberline {10.7}Turning a linear regression model into a curve - polynomial regression}{53}{section.10.7}
-\contentsline {subsection}{\numberline {10.7.1}Modeling nonlinear relationships in the Housing Dataset}{53}{subsection.10.7.1}
-\contentsline {subsection}{\numberline {10.7.2}Dealing with nonlinear relationships using random forests}{53}{subsection.10.7.2}
-\contentsline {subsubsection}{Decision tree regression}{53}{section*.5}
-\contentsline {subsubsection}{Random forest regression}{54}{section*.6}
-\contentsline {section}{\numberline {10.8}Summary}{54}{section.10.8}
-\contentsline {chapter}{\numberline {11}Working with Unlabeled Data -- Clustering Analysis}{55}{chapter.11}
-\contentsline {section}{\numberline {11.1}Grouping objects by similarity using k-means}{55}{section.11.1}
-\contentsline {subsection}{\numberline {11.1.1}K-means++}{56}{subsection.11.1.1}
-\contentsline {subsection}{\numberline {11.1.2}Hard versus soft clustering}{56}{subsection.11.1.2}
-\contentsline {subsection}{\numberline {11.1.3}Using the elbow method to find the optimal number of clusters}{58}{subsection.11.1.3}
-\contentsline {subsection}{\numberline {11.1.4}Quantifying the quality of clustering via silhouette plots}{58}{subsection.11.1.4}
-\contentsline {section}{\numberline {11.2}Organizing clusters as a hierarchical tree}{58}{section.11.2}
-\contentsline {subsection}{\numberline {11.2.1}Performing hierarchical clustering on a distance matrix}{58}{subsection.11.2.1}
-\contentsline {subsection}{\numberline {11.2.2}Attaching dendrograms to a heat map}{58}{subsection.11.2.2}
-\contentsline {subsection}{\numberline {11.2.3}Applying agglomerative clustering via scikit-learn}{58}{subsection.11.2.3}
-\contentsline {section}{\numberline {11.3}Locating regions of high density via DBSCAN}{58}{section.11.3}
-\contentsline {section}{\numberline {11.4}Summary}{59}{section.11.4}
-\contentsline {chapter}{\numberline {12}Training Artificial Neural Networks for Image Recognition}{60}{chapter.12}
-\contentsline {section}{\numberline {12.1}Modeling complex functions with artificial neural networks}{60}{section.12.1}
-\contentsline {subsection}{\numberline {12.1.1}Single-layer neural network recap}{60}{subsection.12.1.1}
-\contentsline {subsection}{\numberline {12.1.2}Introducing the multi-layer neural network architecture}{61}{subsection.12.1.2}
-\contentsline {subsection}{\numberline {12.1.3}Activating a neural network via forward propagation}{62}{subsection.12.1.3}
-\contentsline {section}{\numberline {12.2}Classifying handwritten digits}{63}{section.12.2}
-\contentsline {subsection}{\numberline {12.2.1}Obtaining the MNIST dataset}{63}{subsection.12.2.1}
-\contentsline {subsection}{\numberline {12.2.2}Implementing a multi-layer perceptron}{63}{subsection.12.2.2}
-\contentsline {section}{\numberline {12.3}Training an artificial neural network}{64}{section.12.3}
-\contentsline {subsection}{\numberline {12.3.1}Computing the logistic cost function}{64}{subsection.12.3.1}
-\contentsline {subsection}{\numberline {12.3.2}Training neural networks via backpropagation}{65}{subsection.12.3.2}
-\contentsline {section}{\numberline {12.4}Developing your intuition for backpropagation}{67}{section.12.4}
-\contentsline {section}{\numberline {12.5}Debugging neural networks with gradient checking}{67}{section.12.5}
-\contentsline {section}{\numberline {12.6}Convergence in neural networks}{69}{section.12.6}
-\contentsline {section}{\numberline {12.7}Other neural network architectures}{69}{section.12.7}
-\contentsline {subsection}{\numberline {12.7.1}Convolutional Neural Networks}{69}{subsection.12.7.1}
-\contentsline {subsection}{\numberline {12.7.2}Recurrent Neural Networks}{69}{subsection.12.7.2}
-\contentsline {section}{\numberline {12.8}A few last words about neural network implementation}{69}{section.12.8}
-\contentsline {section}{\numberline {12.9}Summary}{69}{section.12.9}
-\contentsline {chapter}{\numberline {13}Parallelizing Neural Network Training with Theano}{70}{chapter.13}
-\contentsline {section}{\numberline {13.1}Building, compiling, and running expressions with Theano}{70}{section.13.1}
-\contentsline {subsection}{\numberline {13.1.1}What is Theano?}{70}{subsection.13.1.1}
-\contentsline {subsection}{\numberline {13.1.2}First steps with Theano}{70}{subsection.13.1.2}
-\contentsline {subsection}{\numberline {13.1.3}Configuring Theano}{70}{subsection.13.1.3}
-\contentsline {subsection}{\numberline {13.1.4}Working with array structures}{70}{subsection.13.1.4}
-\contentsline {subsection}{\numberline {13.1.5}Wrapping things up -- a linear regression example}{70}{subsection.13.1.5}
-\contentsline {section}{\numberline {13.2}Choosing activation functions for feedforward neural networks}{70}{section.13.2}
-\contentsline {subsection}{\numberline {13.2.1}Logistic function recap}{70}{subsection.13.2.1}
-\contentsline {subsection}{\numberline {13.2.2}Estimating probabilities in multi-class classification via the softmax function}{71}{subsection.13.2.2}
-\contentsline {subsection}{\numberline {13.2.3}Broadening the output spectrum by using a hyperbolic tangent}{71}{subsection.13.2.3}
-\contentsline {section}{\numberline {13.3}Training neural networks efficiently using Keras}{71}{section.13.3}
-\contentsline {section}{\numberline {13.4}Summary}{71}{section.13.4}
+\contentsline {section}{\numberline {6.6}Summary}{39}{section.6.6}
+\contentsline {chapter}{\numberline {7}Combining Different Models for Ensemble Learning}{40}{chapter.7}
+\contentsline {section}{\numberline {7.1}Learning with ensembles}{40}{section.7.1}
+\contentsline {section}{\numberline {7.2}Implementing a simple majority vote classifier}{41}{section.7.2}
+\contentsline {subsection}{\numberline {7.2.1}Combining different algorithms for classification with majority vote}{42}{subsection.7.2.1}
+\contentsline {section}{\numberline {7.3}Evaluating and tuning the ensemble classifier}{42}{section.7.3}
+\contentsline {section}{\numberline {7.4}Bagging -- building an ensemble of classifiers from bootstrap samples}{42}{section.7.4}
+\contentsline {section}{\numberline {7.5}Leveraging weak learners via adaptive boosting}{42}{section.7.5}
+\contentsline {section}{\numberline {7.6}Summary}{44}{section.7.6}
+\contentsline {chapter}{\numberline {8}Applying Machine Learning to Sentiment Analysis}{45}{chapter.8}
+\contentsline {section}{\numberline {8.1}Obtaining the IMDb movie review dataset}{45}{section.8.1}
+\contentsline {section}{\numberline {8.2}Introducing the bag-of-words model}{45}{section.8.2}
+\contentsline {subsection}{\numberline {8.2.1}Transforming words into feature vectors}{45}{subsection.8.2.1}
+\contentsline {subsection}{\numberline {8.2.2}Assessing word relevancy via term frequency-inverse document frequency}{45}{subsection.8.2.2}
+\contentsline {subsection}{\numberline {8.2.3}Cleaning text data}{46}{subsection.8.2.3}
+\contentsline {subsection}{\numberline {8.2.4}Processing documents into tokens}{46}{subsection.8.2.4}
+\contentsline {section}{\numberline {8.3}Training a logistic regression model for document classification}{46}{section.8.3}
+\contentsline {section}{\numberline {8.4}Working with bigger data - online algorithms and out-of-core learning}{46}{section.8.4}
+\contentsline {section}{\numberline {8.5}Summary}{46}{section.8.5}
+\contentsline {chapter}{\numberline {9}Embedding a Machine Learning Model into a Web Application}{47}{chapter.9}
+\contentsline {section}{\numberline {9.1}Chapter 8 recap - Training a model for movie review classification}{47}{section.9.1}
+\contentsline {section}{\numberline {9.2}Serializing fitted scikit-learn estimators}{47}{section.9.2}
+\contentsline {section}{\numberline {9.3}Setting up a SQLite database for data storage Developing a web application with Flask}{47}{section.9.3}
+\contentsline {section}{\numberline {9.4}Our first Flask web application}{47}{section.9.4}
+\contentsline {subsection}{\numberline {9.4.1}Form validation and rendering}{47}{subsection.9.4.1}
+\contentsline {subsection}{\numberline {9.4.2}Turning the movie classifier into a web application}{47}{subsection.9.4.2}
+\contentsline {section}{\numberline {9.5}Deploying the web application to a public server}{47}{section.9.5}
+\contentsline {subsection}{\numberline {9.5.1}Updating the movie review classifier}{47}{subsection.9.5.1}
+\contentsline {section}{\numberline {9.6}Summary}{47}{section.9.6}
+\contentsline {chapter}{\numberline {10}Predicting Continuous Target Variables with Regression Analysis}{48}{chapter.10}
+\contentsline {section}{\numberline {10.1}Introducing a simple linear regression model}{48}{section.10.1}
+\contentsline {section}{\numberline {10.2}Exploring the Housing Dataset}{48}{section.10.2}
+\contentsline {subsection}{\numberline {10.2.1}Visualizing the important characteristics of a dataset}{48}{subsection.10.2.1}
+\contentsline {section}{\numberline {10.3}Implementing an ordinary least squares linear regression model}{50}{section.10.3}
+\contentsline {subsection}{\numberline {10.3.1}Solving regression for regression parameters with gradient descent}{50}{subsection.10.3.1}
+\contentsline {subsection}{\numberline {10.3.2}Estimating the coefficient of a regression model via scikit-learn}{50}{subsection.10.3.2}
+\contentsline {section}{\numberline {10.4}Fitting a robust regression model using RANSAC}{50}{section.10.4}
+\contentsline {section}{\numberline {10.5}Evaluating the performance of linear regression models}{50}{section.10.5}
+\contentsline {section}{\numberline {10.6}Using regularized methods for regression}{51}{section.10.6}
+\contentsline {section}{\numberline {10.7}Turning a linear regression model into a curve - polynomial regression}{52}{section.10.7}
+\contentsline {subsection}{\numberline {10.7.1}Modeling nonlinear relationships in the Housing Dataset}{52}{subsection.10.7.1}
+\contentsline {subsection}{\numberline {10.7.2}Dealing with nonlinear relationships using random forests}{52}{subsection.10.7.2}
+\contentsline {subsubsection}{Decision tree regression}{52}{section*.5}
+\contentsline {subsubsection}{Random forest regression}{53}{section*.6}
+\contentsline {section}{\numberline {10.8}Summary}{53}{section.10.8}
+\contentsline {chapter}{\numberline {11}Working with Unlabeled Data -- Clustering Analysis}{54}{chapter.11}
+\contentsline {section}{\numberline {11.1}Grouping objects by similarity using k-means}{54}{section.11.1}
+\contentsline {subsection}{\numberline {11.1.1}K-means++}{55}{subsection.11.1.1}
+\contentsline {subsection}{\numberline {11.1.2}Hard versus soft clustering}{55}{subsection.11.1.2}
+\contentsline {subsection}{\numberline {11.1.3}Using the elbow method to find the optimal number of clusters}{57}{subsection.11.1.3}
+\contentsline {subsection}{\numberline {11.1.4}Quantifying the quality of clustering via silhouette plots}{57}{subsection.11.1.4}
+\contentsline {section}{\numberline {11.2}Organizing clusters as a hierarchical tree}{57}{section.11.2}
+\contentsline {subsection}{\numberline {11.2.1}Performing hierarchical clustering on a distance matrix}{57}{subsection.11.2.1}
+\contentsline {subsection}{\numberline {11.2.2}Attaching dendrograms to a heat map}{57}{subsection.11.2.2}
+\contentsline {subsection}{\numberline {11.2.3}Applying agglomerative clustering via scikit-learn}{57}{subsection.11.2.3}
+\contentsline {section}{\numberline {11.3}Locating regions of high density via DBSCAN}{57}{section.11.3}
+\contentsline {section}{\numberline {11.4}Summary}{58}{section.11.4}
+\contentsline {chapter}{\numberline {12}Training Artificial Neural Networks for Image Recognition}{59}{chapter.12}
+\contentsline {section}{\numberline {12.1}Modeling complex functions with artificial neural networks}{59}{section.12.1}
+\contentsline {subsection}{\numberline {12.1.1}Single-layer neural network recap}{59}{subsection.12.1.1}
+\contentsline {subsection}{\numberline {12.1.2}Introducing the multi-layer neural network architecture}{60}{subsection.12.1.2}
+\contentsline {subsection}{\numberline {12.1.3}Activating a neural network via forward propagation}{61}{subsection.12.1.3}
+\contentsline {section}{\numberline {12.2}Classifying handwritten digits}{62}{section.12.2}
+\contentsline {subsection}{\numberline {12.2.1}Obtaining the MNIST dataset}{62}{subsection.12.2.1}
+\contentsline {subsection}{\numberline {12.2.2}Implementing a multi-layer perceptron}{62}{subsection.12.2.2}
+\contentsline {section}{\numberline {12.3}Training an artificial neural network}{63}{section.12.3}
+\contentsline {subsection}{\numberline {12.3.1}Computing the logistic cost function}{63}{subsection.12.3.1}
+\contentsline {subsection}{\numberline {12.3.2}Training neural networks via backpropagation}{64}{subsection.12.3.2}
+\contentsline {section}{\numberline {12.4}Developing your intuition for backpropagation}{66}{section.12.4}
+\contentsline {section}{\numberline {12.5}Debugging neural networks with gradient checking}{66}{section.12.5}
+\contentsline {section}{\numberline {12.6}Convergence in neural networks}{68}{section.12.6}
+\contentsline {section}{\numberline {12.7}Other neural network architectures}{68}{section.12.7}
+\contentsline {subsection}{\numberline {12.7.1}Convolutional Neural Networks}{68}{subsection.12.7.1}
+\contentsline {subsection}{\numberline {12.7.2}Recurrent Neural Networks}{68}{subsection.12.7.2}
+\contentsline {section}{\numberline {12.8}A few last words about neural network implementation}{68}{section.12.8}
+\contentsline {section}{\numberline {12.9}Summary}{68}{section.12.9}
+\contentsline {chapter}{\numberline {13}Parallelizing Neural Network Training with Theano}{69}{chapter.13}
+\contentsline {section}{\numberline {13.1}Building, compiling, and running expressions with Theano}{69}{section.13.1}
+\contentsline {subsection}{\numberline {13.1.1}What is Theano?}{69}{subsection.13.1.1}
+\contentsline {subsection}{\numberline {13.1.2}First steps with Theano}{69}{subsection.13.1.2}
+\contentsline {subsection}{\numberline {13.1.3}Configuring Theano}{69}{subsection.13.1.3}
+\contentsline {subsection}{\numberline {13.1.4}Working with array structures}{69}{subsection.13.1.4}
+\contentsline {subsection}{\numberline {13.1.5}Wrapping things up -- a linear regression example}{69}{subsection.13.1.5}
+\contentsline {section}{\numberline {13.2}Choosing activation functions for feedforward neural networks}{69}{section.13.2}
+\contentsline {subsection}{\numberline {13.2.1}Logistic function recap}{69}{subsection.13.2.1}
+\contentsline {subsection}{\numberline {13.2.2}Estimating probabilities in multi-class classification via the softmax function}{70}{subsection.13.2.2}
+\contentsline {subsection}{\numberline {13.2.3}Broadening the output spectrum by using a hyperbolic tangent}{70}{subsection.13.2.3}
+\contentsline {section}{\numberline {13.3}Training neural networks efficiently using Keras}{70}{section.13.3}
+\contentsline {section}{\numberline {13.4}Summary}{70}{section.13.4}