fix syntax

doc/omlw2014/presentation.tex

@@ -103,9 +103,9 @@ Montréal, Canada \newline
 \end{frame}
 
 \begin{frame}{Why scripting for GPUs?}
-\begin{bf}They Complement each other\end{bf}
+  \begin{bf}They Complement each other\end{bf}
 
-GPUs are everything that scripting/high level languages are not
+  GPUs are everything that scripting/high level languages are not
 
   \begin{itemize}
     \item Highly parallel
@@ -113,20 +113,19 @@ GPUs are everything that scripting/high level languages are not
     \item Built for maximum FP/memory throughput
     \item So hard to program that meta-programming is easier.
   \end{itemize}
-\end{itemize}
 
-\begin{bf}Best of both:\end{bf} easy scripted development invokes GPU kernel.
+  \begin{bf}Best of both:\end{bf} easy scripted development invokes GPU kernel.
 
-\begin{bf}Theano C code generation removes overhead\end{bf} from
-function call between Python and C, by launching many C functions at once.
+  \begin{bf}Theano C code generation removes overhead\end{bf} from
+  function call between Python and C, by launching many C functions at once.
 
 \end{frame}
 
 \begin{frame}{Theano}
 
-High-level domain-specific language tailored to numeric computation.
+  High-level domain-specific language tailored to numeric computation.
 
-\begin{itemize}
+  \begin{itemize}
     \item Syntax as close to NumPy as possible
     \item Compiles most common expressions to C for CPU and GPU
     \item Limited expressivity means lots of opportunities for expression-level optimizations
@@ -145,27 +144,27 @@ High-level domain-specific language tailored to numeric computation.
     \item Automatic differentiation and R op
 
     \item Sparse matrices
-\end{itemize}
+  \end{itemize}
 \end{frame}
 
 
 \begin{frame}{Pylearn2}
 
-Machine Learning library aimed at researchers
+  Machine Learning library aimed at researchers
 
-\begin{itemize}
+  \begin{itemize}
     \item Built on top of Theano, for fast execution and use of GPU
     \item Easy to try variants of implemented algorithms, and to extend them (using Theano)
     \item Contains a collection of:
     \begin{itemize}
       \item Training Algorithms (i.e., Stochastic and Batch GD, model-specific rules)
-    \item Costs and Estimation Criteria, supervised and unsupervised (NLL, Score Matching, NCE, ...)
-    \item Models (NNets, ConvNets, RBMs, k-means, PCA, SVMs, ...)
+      \item Costs and Estimation Criteria, supervised and unsupervised (NLL, Score Matching, NCE)
+      \item Models (NNets, ConvNets, RBMs, k-means, PCA, SVMs)
       \item Datasets (MNIST, CIFAR-10) and preprocessors (LCN, ZCA)
     \end{itemize}
     \item Experiments can be specified through a YAML config file, or by a Python script
     \item Scripts for visualizing weights, plot monitored values during training
-\end{itemize}
+  \end{itemize}
 
 \end{frame}