.. _tutorials:

=========
Tutorials
=========

.. rubric:: Scope and intention

This page presents a collection of tutorial written by the authors of mprod package
and intended to help newcomers in incorporating the machinery offered by the library
in their analysis workflows.

The main (and only) data-scientific tool currently implemented is the TCAM
dimensionality reduction algorithm :footcite:p:`mor2021`. We intend to keep expanding the
package content by adding :math:`\star_{\mathbf{M}}`-product based tools
(such as tensor-PLS, tensor-CCA), and we encourage any form of collaboration,
hoping to get good responses, feedback and help from the data-science community.

.. rubric:: Target audience

We do not expect expertise in Machine Learning, or data science, in order to use this package.
In fact, it is aimed at non-experts

That said, the library is not - by any means - meant to serve as a **black magic tensor package for dummies**.
Just like with almost everything in machine-learning, using this library for ML related tasks require **some** general
mathematical understanding of ML concepts.
The implementation of dimensionality reduction methods (currently TCAM), is made consistent with
`scikit-learn <https://scikit-learn.org/>`_ library to the maximum possible extent, in order to enable smooth
integration within the pythonic ML ecosystem.
For this reason, the users are assumed to know the `scikit-learn <https://scikit-learn.org/>`_ library.
Scikit-learn package offers fantastic documentation, tutorials and examples that are more than enough in order to get
started with machine learning in no time.

.. note::

   We acknowledge that many potential users might find R more familiar.
   However, we urge them to take the time and try the alternative.

In addition, deep understanding of the mathematical theory underlying  mprod based tensor algorithms is always a good
idea. Bellow, you can find a short :ref:`Primer` section  about the idea behind tensor-tensor algebra via the
:math:`\star_{\bf{M}}` -product framework (For a thorough introduction, we refer the interested readers to
:footcite:p:`Kilmer`)

The :ref:`TCAM` section contains tutorials for working with :class:`mprod.dimensionality_reduction.TCAM`.
For construction and showcase of TCAM refer to :footcite:p:`mor2021`


--------------------------------


.. _TCAM:

----
TCAM
----
.. toctree::
   :maxdepth: 8

   basic_example
   supervised_learning

.. Schirmer2018




.. _Primer:

------------
⚙ Background
------------
.. toctree::
   :maxdepth: 4

   mprod_primer


.. footbibliography::