pylearn2 slide

doc/omlw2014/presentation.tex

@@ -124,12 +124,52 @@ function call between Python and C, by launching many C functions at once.
 
 \begin{frame}{Theano}
 
+High-level domain-specific language tailored to numeric computation.
 
+\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
+  \begin{itemize}
+    \item No subroutines -> global optimization
+    \item Strongly typed -> compiles to machine instructions
+    \item Array oriented -> easy parallelism
+    \item Support for looping and branching in expressions
+  \end{itemize}
+  \item Expression substitution optimizations automatically draw
+  on many back-end technologies for best performance.
+  \begin{itemize}
+    \item BLAS, SciPy, Cython, CUDA
+    \item Slower fallbacks always available
+  \end{itemize}
+  \item Automatic differentiation and R op
+
+  \item Sparse matrices
+\end{itemize}
 \end{frame}
 
 
 \begin{frame}{Pylearn2}
+
+Machine Learning library aimed at researchers
+
+\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 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{frame}
+
+
 \begin{frame}{libgpuarray}
 \end{frame}