Remove primers for Python and NumPy from the documentation

doc/tutorial/index.rst

@@ -22,13 +22,6 @@ Throughout the tutorial, bear in mind that there is a :ref:`glossary` as well
 as *index* and *modules* links in the upper-right corner of each page to help
 you out.
 
-Prerequisites
--------------
-.. toctree::
-
-    python
-    numpy
-
 Basics
 ------
 

doc/tutorial/numpy.rst

-
-.. _numpy:
-
-.. testsetup::
-
-   import numpy
-
-***************
-NumPy refresher
-***************
-
-Here are some quick guides to NumPy:
-  * `NumPy for MATLAB users <https://numpy.org/doc/stable/user/numpy-for-matlab-users.html>`__
-  * `Numpy User Guide <http://docs.scipy.org/doc/numpy/user/index.html>`__
-  * `More detailed Numpy tutorial <https://scipy.github.io/old-wiki/pages/Tentative_NumPy_Tutorial>`__
-  * `100 NumPy exercises <https://github.com/rougier/numpy-100>`__
-  * `From Python to Numpy free book <https://www.labri.fr/perso/nrougier/from-python-to-numpy/>`__
-
-    .. [TODO: More doc, e.g. see _test_tensor.py]
-
-
-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 
-where each example has dimension 5. If this would be the input of a
-neural network then the weights from the input to the first hidden
-layer would represent a matrix of size (5, #hid). 
-
-Consider this 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.
-
-Broadcasting
-============
-
-Numpy does *broadcasting* of arrays of different shapes during
-arithmetic operations. What this means in general is that the smaller 
-array (or scalar) is *broadcasted* across the larger array so that they have
-compatible shapes. The example below shows an instance of
-*broadcasting*:
-
->>> a = numpy.asarray([1.0, 2.0, 3.0])
->>> b = 2.0
->>> a * b
-array([ 2.,  4.,  6.])
-
-The smaller array ``b`` (actually a scalar here, which works like a 0-d array) in this case is *broadcasted* to the same size
-as ``a`` during the multiplication. This trick is often useful in
-simplifying how expression are written. More detail about *broadcasting*
-can be found in the `numpy user guide <http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`__.

doc/tutorial/python.rst

-.. _python:
-
-
-***************
-Python tutorial
-***************
-
-In this documentation, we suppose that the reader knows Python. Here is a small list of Python 
-tutorials/exercises if you need to learn it or only need a refresher:
-
-  * `Python Challenge <http://www.pythonchallenge.com/>`__
-  * `Dive into Python <http://diveintopython.net/>`__
-  * `Google Python Class <https://developers.google.com/edu/python/>`__
-  * `Enthought Python course <https://training.enthought.com/?utm_source=academic&utm_medium=email&utm_campaign=EToD-Launch#/courses>`__ (free for academics)