Added numpy.txt

doc/contents.txt

@@ -11,6 +11,7 @@ Contents
    LICENSE
    introduction
    install
+   numpy
    basic_tutorial/index
    advanced_tutorial/index
    advanced/index

doc/numpy.txt

+
+.. _numpy:
+
+
+===============
+NumPy refresher
+===============
+
+
+---------------------------------------
+Matrix conventions for machine learning
+---------------------------------------
+
+Rows are horizontal and columns are vertical.
+Every row is an example. Therefore, inputs[10,5] is a matrix of 10 examples with 5 dimensions per.
+So to make a NN out of it, multiply by a weight matrix of size (5, #hid).
+
+If I have an array:
+
+>>> numpy.asarray([[1., 2], [3, 4], [5, 6]])
+array([[ 1.,  2.],
+       [ 3.,  4.],
+       [ 5.,  6.]])
+>>> numpy.asarray([[1., 2], [3, 4], [5, 6]]).shape
+(3, 2)
+
+This is a 3x2 matrix, i.e. there are 3 rows and 2 columns.
+
+To access the entry in the 3rd row (row #2) and the 1st column (column #0):
+
+>>> numpy.asarray([[1., 2], [3, 4], [5, 6]])[2,0]
+5.0
+
+
+To remember this, keep in mind that we read left-to-right, top-to-bottom,
+so each thing that is contiguous is a row.  That is, there are 3 rows
+and 2 columns.