Merge pull request #6 from dwf/theano_vision

doc/introduction.txt

@@ -140,66 +140,68 @@ A PDF version of the online documentation may be found `here
 Theano Vision
 =============
 
-This is the vision we have for Theano. This is to help people what to
-expect in the futur for Theano, but we don't promise to implement all
-that. This also should help you to understand where Theano fix related
-to all other computational tools.
-
-* Support tensor and sparse operation
-* Support linear algebra operation
-* Graph Transformation
+This is the vision we have for Theano. This is give people an idea of what to
+expect in the future of Theano, but we can't promise to implement all
+of it. This should also help you to understand where Theano fits in relation
+to other computational tools.
+
+* Support tensor and sparse operations
+* Support linear algebra operations
+* Graph Transformations
     * Differentiation/higher order differentiation
-    * R operation
-    * Speed/memory optimization
-    * Numeriacal stability optimization
-* Have an OpenCL back-end (for GPU, SIMD and multi-core)
+    * 'R' and 'L' differential operators
+    * Speed/memory optimizations
+    * Numerical stability optimizations
+* Have an OpenCL backend (for GPU, SIMD and multi-core)
 * Lazy evaluation
 * Loop
 * Parallel execution (SIMD, multi-core, multi-node on cluster,
   multi-node distributed)
-* Support all numpy/scipy functionality
-* Easy wrapping of library function in Theano
+* Support all NumPy/basic SciPy functionality
+* Easy wrapping of library functions in Theano
 
-Note: There is no short term plan to work to make Theano work in multi-node in one Theano function.
+Note: There is no short term plan to enable multi-node computation in one
+Theano function.
 
 Theano Vision State
 ===================
 
-Here is the state of that vision as of 24 October 2011 (after Theano release 0.4.1):
+Here is the state of that vision as of 24 October 2011 (after Theano release
+0.4.1):
 
-* We support tensor by using the numpy.ndarray object and we have operations on them.
-* We support sparse by using the scipy.{csc,csr}_matrix object and have some operation on them (More are comming).
-* We have a start of more advanced linear algebra operations.
-* We have many graph transformation that cover the 4 categories.
+* We support tensors using the `numpy.ndarray` object and we support many operations on them.
+* We support sparse types by using the `scipy.{csc,csr}_matrix` object and support some operations on them (more are coming).
+* We have started implementing/wrapping more advanced linear algebra operations.
+* We have many graph transformations that cover the 4 categories listed above.
 * We can improve the graph transformation with better storage optimization
-  and instruction selection
+  and instruction selection.
     * Similar to auto-tuning during the optimization phase, but this
-      don't apply to only 1 op.
+      doesn't apply to only 1 op.
     * Example of use: Determine if we should move computation to the
-      gpu or not depending of the input size.
+      GPU or not depending on the input size.
     * Possible implementation note: allow Theano Variable in the env to
       have more then 1 owner.
-* We have a CUDA back-end for tensor of float32 only.
-* Make a generic GPU nd array(GPU tensor) (started in the
+* We have a CUDA backend for tensors of type `float32` only.
+* Efforts have begun towards a generic GPU ndarray (GPU tensor) (started in the
   `compyte <https://github.com/inducer/compyte/wiki>`_ project)
-    * Move GPU backend outside of Theano(on top of PyCUDA/PyOpenCL)
+    * Move GPU backend outside of Theano (on top of PyCUDA/PyOpenCL)
     * Will allow GPU to work on Windows and use an OpenCL backend on CPU.
-* Loop work, but not all related optimization done.
-* The cvm linker allow lazy evaluation. It work but some work still needed
-  to enable it by default.
-    * All test pass with linker=cvm?
-    * How to have DEBUG_MODE check it? Now DebugMode check it non lazily.
-    * The profiler using by cvm is less complete then PROFILE_MODE.
-* SIMD parallism on the cpu come from the compiler
-* Multi-core parallism is only supported for gemv, gemm if the external
-  implementation of it implement it.
+* Loops work, but not all related optimizations are currently done.
+* The cvm linker allows lazy evaluation. It works, but some work is still
+  needed before enabling it by default.
+    * All tests pass with linker=cvm?
+    * How to have `DEBUG_MODE` check it? Right now, DebugMode checks the computation non-lazily.
+    * The profiler used by cvm is less complete than `PROFILE_MODE`.
+* SIMD parallelism on the CPU comes from the compiler.
+* Multi-core parallelism is only supported for gemv and gemm, and only
+  if the external BLAS implementation supports it.
 * No muli-node implementation in one Theano experiment.
-* Many, but not all numpy function/alias implemented.
+* Many, but not all NumPy functions/aliases are implemented.
     * http://trac-hg.assembla.com/theano/ticket/781
-* Wrapping an existing python function in easy, but better documentation of
+* Wrapping an existing Python function in easy, but better documentation of
   it would make it even easier.
-* We need to find a way to separate the Shared variable data memory
-  storage location vs object type(tensor, sparse, dtype, broadcast
+* We need to find a way to separate the shared variable memory
+  storage location from its object type (tensor, sparse, dtype, broadcast
   flags).