Update docs markup (#1813) (#1897)

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diff --git a/docs/source/_static/style.css b/docs/source/_static/style.css
@@ -4,7 +4,7 @@
    .wy-table-responsive table td {
       /* !important prevents the common CSS stylesheets from overriding
          this as on RTD they are loaded after this stylesheet */
-         
+
       white-space: normal !important;
    }
 }

diff --git a/docs/source/api/algorithms/index.rst b/docs/source/api/algorithms/index.rst
@@ -27,7 +27,7 @@ Refer to :ref:`Developer Guide <dg_algorithms>` for mathematical descriptions of
    clustering/index.rst
    covariance/index.rst
    decomposition/index.rst
-   ensembles/index.rst   
+   ensembles/index.rst
    kernel-functions/index.rst
    nearest-neighbors/index.rst
    pairwise-distances/index.rst

diff --git a/docs/source/bibliography.rst b/docs/source/bibliography.rst
@@ -24,19 +24,19 @@ For more information about algorithms implemented in |short_name|, refer to the
 .. [Adams2003]
    Adams, Robert A., and John JF Fournier. Sobolev spaces. Vol. 140. Elsevier, 2003
 
-.. [Agrawal94] 
+.. [Agrawal94]
    Rakesh Agrawal, Ramakrishnan Srikant. *Fast Algorithms for Mining
    Association Rules*. Proceedings of the 20th VLDB Conference
    Santiago, Chile, 1994.
 
-.. [Arthur2007] 
+.. [Arthur2007]
    Arthur, D., Vassilvitskii, S. *k-means++: The Advantages of
    Careful Seeding*. Proceedings of the eighteenth annual ACM-SIAM
    symposium on Discrete algorithms. Society for Industrial and
    Applied Mathematics Philadelphia, PA, USA, 2007, pp. 1027-1035.
    Available from http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf.
 
-.. [Bahmani2012] 
+.. [Bahmani2012]
    B. Bahmani, B. Moseley, A. Vattani, R. Kumar, S. Vassilvitskii.
    *Scalable K-means++*. Proceedings of the VLDB Endowment, 2012.
    Available from
@@ -56,39 +56,39 @@ For more information about algorithms implemented in |short_name|, refer to the
    BACON: blocked adaptive computationally efficient outlier nominators.
    Computational Statistics & Data Analysis, 34, 279-298, 2000.
 
-.. [Bishop2006] 
+.. [Bishop2006]
    Christopher M. Bishop. *Pattern Recognition and Machine Learning*,
    p.198, Computational Statistics & Data Analysis, 34, 279-298,
    2000. Springer Science+Business Media, LLC, ISBN-10:
    0-387-31073-8, 2006.
 
-.. [Boser92] 
+.. [Boser92]
    B. E. Boser, I. Guyon, and V. Vapnik. *A training algorithm for
    optimal marginclassiﬁers.*. Proceedings of the Fifth Annual
    Workshop on Computational Learning Theory, pp: 144–152, ACM Press,
    1992.
 
-.. [Breiman2001] 
+.. [Breiman2001]
    Leo Breiman. *Random Forests*. Machine Learning, Volume 45 Issue
    1, pp. 5-32, 2001.
 
-.. [Breiman84] 
+.. [Breiman84]
    Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J.
    Stone. *Classification and Regression Trees*. Chapman & Hall,
    1984.
 
-.. [Bro07] 
+.. [Bro07]
    Bro, R.; Acar, E.; Kolda, T.. *Resolving the sign ambiguity in the
    singular value decomposition*. SANDIA Report, SAND2007-6422,
    Unlimited Release, October, 2007.
 
-.. [Byrd2015] 
+.. [Byrd2015]
    R. H. Byrd, S. L. Hansen, Jorge Nocedal, Y. Singer. *A Stochastic
    Quasi-Newton Method for Large-Scale Optimization*, 2015.
    arXiv:1401.7020v2 [math.OC]. Available from
    http://arxiv.org/abs/1401.7020v2.
 
-.. [Chen2016] 
+.. [Chen2016]
    T. Chen, C. Guestrin. *XGBoost: A Scalable Tree Boosting System*,
    KDD '16 Proceedings of the 22nd ACM SIGKDD International
    Conference on Knowledge Discovery and Data Mining.
@@ -102,7 +102,7 @@ For more information about algorithms implemented in |short_name|, refer to the
    J. W. Demmel and W. Kahan. *Accurate singular values of
    bidiagonal matrices*. SIAM J. Sci. Stat. Comput., 11 (1990), pp. 873-912.
 
-.. [Dempster77] 
+.. [Dempster77]
    A.P.Dempster, N.M. Laird, and D.B. Rubin. *Maximum-likelihood from
    incomplete data via the em algorithm*. J. Royal Statist. Soc. Ser.
    B., 39, 1977.
@@ -118,7 +118,7 @@ For more information about algorithms implemented in |short_name|, refer to the
    In Proceedings of the 2nd ACM International Conference on Knowledge Discovery and Data Mining (KDD).
    226-231, 1996.
 
-.. [Fan05] 
+.. [Fan05]
    Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin. *Working Set Selection
    Using Second Order Information for Training Support Vector
    Machines.*. Journal of Machine Learning Research 6 (2005), pp:
@@ -129,12 +129,12 @@ For more information about algorithms implemented in |short_name|, refer to the
    Algorithmic Aspects in Information and Management.
    4th International conference, AAIM 2008, Shanghai, China, June 23-25, 2008. Proceedings, Springer.
 
-.. [Freund99] 
+.. [Freund99]
    Yoav Freund, Robert E. Schapire. *Additive Logistic regression: a
    statistical view of boosting*. Journal of Japanese Society for
    Artificial Intelligence (14(5)), 771-780, 1999.
 
-.. [Friedman98] 
+.. [Friedman98]
    Friedman, Jerome H., Trevor J. Hastie and Robert Tibshirani.
    *Additive Logistic Regression: a Statistical View of Boosting.*.
    1998.
@@ -144,12 +144,12 @@ For more information about algorithms implemented in |short_name|, refer to the
    Additive Logistic regression: a statistical view of boosting.
    The Annals of Statistics, 28(2), pp: 337-407, 2000.
 
-.. [Friedman2010] 
+.. [Friedman2010]
    Friedman, Jerome, Trevor Hastie, and Rob Tibshirani.
    *Regularization paths for generalized linear models via coordinate
    descent.*. Journal of statistical software 33.1 (2010): 1.
 
-.. [Friedman2017] 
+.. [Friedman2017]
    Jerome Friedman, Trevor Hastie, Robert Tibshirani. 2017. *The
    Elements of Statistical Learning Data Mining, Inference, and
    Prediction.* Springer.
@@ -161,18 +161,18 @@ For more information about algorithms implemented in |short_name|, refer to the
 .. [Gross2014]
    J. Gross, J. Yellen, P. Zhang, Handbook of Graph Theory, Second Edition, 2014.
 
-.. [Hastie2009] 
+.. [Hastie2009]
    Trevor Hastie, Robert Tibshirani, Jerome Friedman. *The Elements
    of Statistical Learning: Data Mining, Inference, and Prediction*.
    Second Edition (Springer Series in Statistics), Springer, 2009.
    Corr. 7th printing 2013 edition (December 23, 2011).
 
-.. [Hoerl70] 
+.. [Hoerl70]
    Arthur E. Hoerl and Robert W. Kennard. *Ridge Regression: Biased
    Estimation for Nonorthogonal Problems*. Technometrics, Vol. 12,
    No. 1 (Feb., 1970), pp. 55-67.
 
-.. [Hsu02] 
+.. [Hsu02]
    Chih-Wei Hsu and Chih-Jen Lin. *A Comparison of Methods for
    Multiclass Support Vector Machines*. IEEE Transactions on Neural
    Networks, Vol. 13, No. 2, pp: 415-425, 2002.
@@ -182,13 +182,13 @@ For more information about algorithms implemented in |short_name|, refer to the
    Collaborative Filtering for Implicit Feedback Datasets.
    ICDM'08. Eighth IEEE International Conference, 2008.
 
-.. [James2013] 
+.. [James2013]
    Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani.
    *An Introduction to Statistical Learning with Applications in R*.
    Springer Series in Statistics, Springer, 2013 (Corrected at
    6\ :sup:`th` printing 2015).
 
-.. [Joachims99] 
+.. [Joachims99]
    Thorsten Joachims. *Making Large-Scale SVM Learning Practical*.
    Advances in Kernel Methods - Support Vector Learning, B.
    Schölkopf, C. Burges, and A. Smola (ed.), pp: 169 – 184, MIT Press
@@ -198,12 +198,12 @@ For more information about algorithms implemented in |short_name|, refer to the
    S. Lang. *Linear Algebra*. Springer-Verlag New York, 1987.
 
 .. [Li2015]
-   Li, Shengren, and Nina Amenta. 
+   Li, Shengren, and Nina Amenta.
    "Brute-force k-nearest neighbors search on the GPU."
    In International Conference on Similarity Search and Applications, pp. 259-270.
    Springer, Cham, 2015.
 
-.. [Lloyd82] 
+.. [Lloyd82]
    Stuart P Lloyd. *Least squares quantization in PCM*. IEEE
    Transactions on Information Theory 1982, 28 (2): 1982pp: 129–137.
 
@@ -219,10 +219,10 @@ For more information about algorithms implemented in |short_name|, refer to the
    1998, Ed. Niederreiter, H. and Spanier, J., Springer 2000, pp. 56-69,
    available from http://www.math.sci.hiroshima-u.ac.jp/%7Em-mat/MT/DC/dc.html.
 
-.. [Mitchell97] 
+.. [Mitchell97]
    Tom M. Mitchell. *Machine Learning*. McGraw-Hill Education, 1997.
 
-.. [Mu2014] 
+.. [Mu2014]
    Mu Li, Tong Zhang, Yuqiang Chen, Alexander J. Smola. *Efficient
    Mini-batch Training for Stochastic Optimization*, 2014. Available
    from https://www.cs.cmu.edu/~muli/file/minibatch_sgd.pdf.
@@ -232,7 +232,7 @@ For more information about algorithms implemented in |short_name|, refer to the
    Version:2.1 Document Revision:24
    Available from `opencl-2.1.pdf <https://www.khronos.org/registry/OpenCL/specs/opencl-2.1.pdf>`_
 
-.. [Patwary2016] 
+.. [Patwary2016]
    Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan
    Sundaram, Jialin Liu, Peter Sadowski, Evan Racah, Suren Byna,
    Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey. *PANDA: Extreme
@@ -248,21 +248,21 @@ For more information about algorithms implemented in |short_name|, refer to the
    A fast algorithm for training support vector machines." (1998).
    Available from https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf.
 
-.. [Quinlan86] 
+.. [Quinlan86]
    J. R. Quinlan. *Induction of Decision Trees*. Machine Learning,
    Volume 1 Issue 1, pp. 81-106, 1986.
 
-.. [Quinlan87] 
+.. [Quinlan87]
    J. R. Quinlan. *Simplifying decision trees*. International journal
    of Man-Machine Studies, Volume 27 Issue 3, pp. 221-234, 1987.
 
-.. [Renie03] 
+.. [Renie03]
    Jason D.M. Rennie, Lawrence, Shih, Jaime Teevan, David R. Karget.
    *Tackling the Poor Assumptions of Naïve Bayes Text classifiers*.
    Proceedings of the Twentieth International Conference on Machine
    Learning (ICML-2003), Washington DC, 2003.
 
-.. [Rumelhart86] 
+.. [Rumelhart86]
    David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams.
    *Learning representations by back-propagating errors*. Nature
    (323), pp. 533-536, 1986.
@@ -277,7 +277,7 @@ For more information about algorithms implemented in |short_name|, refer to the
    integrates OpenCL™ devices with modern C++, Version 1.2.1 Available from
    `sycl-1.2.1.pdf <https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf>`_
 
-.. [Tan2005] 
+.. [Tan2005]
    Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to
    Data Mining, (First Edition) Addison-Wesley Longman Publishing
    Co., Inc. Boston, MA, USA, 2005, ISBN: 032132136.
@@ -291,13 +291,13 @@ For more information about algorithms implemented in |short_name|, refer to the
 .. [Wen2018]
     Wen, Zeyi, Jiashuai Shi, Qinbin Li, Bingsheng He, and Jian Chen.
     ThunderSVM: A fast SVM library on GPUs and CPUs.
-    The Journal of Machine Learning Research, 19, 1-5 (2018). 
+    The Journal of Machine Learning Research, 19, 1-5 (2018).
 
-.. [Wu04] 
+.. [Wu04]
    Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng. *Probability Estimates
    for Multi-class Classification by Pairwise Coupling*. Journal of
    Machine Learning Research 5, pp: 975-1005, 2004.
 
-.. [Zhu2005] 
+.. [Zhu2005]
    Zhu, Ji, Hui Zou, Saharon Rosset and Trevor J. Hastie.
    *Multi-class AdaBoost*. 2005
diff --git a/docs/source/daal/algorithms/association_rules/association-rules.rst b/docs/source/daal/algorithms/association_rules/association-rules.rst
@@ -73,7 +73,9 @@ The association rules algorithm accepts the input described below.
 Pass the ``Input ID`` as a parameter to the methods that provide input
 for your algorithm.
 
-.. list-table::
+.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|
+
+.. list-table:: Algorithm Input for Association Rules (Batch Processing)
    :widths: 10 60
    :header-rows: 1
    :align: left
@@ -93,10 +95,13 @@ Algorithm Parameters
 
 The association rules algorithm has the following parameters:
 
-.. list-table::
+.. tabularcolumns::  |\Y{0.15}|\Y{0.15}|\Y{0.7}|
+
+.. list-table:: Algorithm Parameters for Association Rules (Batch Processing)
    :widths: 10 10 60
    :header-rows: 1
    :align: left
+   :class: longtable
 
    * - Parameter
      - Default Value
@@ -151,10 +156,13 @@ The association rules algorithm calculates the result described
 below. Pass the ``Result ID`` as a parameter to the methods that access
 the results of your algorithm.
 
-.. list-table::
+.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|
+
+.. list-table:: Algorithm Output for Association Rules (Batch Processing)
    :widths: 10 60
    :header-rows: 1
    :align: left
+   :class: longtable
 
    * - Result ID
      - Result
@@ -163,28 +171,28 @@ the results of your algorithm.
        the table equals the number of items in the large item sets. Each row
        contains two integers:
 
-        + ID of the large item set, the number between 0 and nLargeItemsets -1.
-        + ID of the item, the number between 0 and :math:`nUniqueItems-1`.
+       + ID of the large item set, the number between 0 and nLargeItemsets -1.
+       + ID of the item, the number between 0 and :math:`nUniqueItems-1`.
 
    * - ``largeItemsetsSupport``
      - Pointer to the :math:`nLargeItemsets \times 2` numeric table of support values. Each row contains two integers:
 
-        + ID of the large item set, the number between 0 and nLargeItemsets-1.
-        + The support value, the number of times the item set is met in the array of transactions.
+       + ID of the large item set, the number between 0 and nLargeItemsets-1.
+       + The support value, the number of times the item set is met in the array of transactions.
 
    * - ``antecedentItemsets``
      - Pointer to the :math:`nAntecedentItems \times 2` numeric table that contains the
        left-hand-side (X) part of the association rules. Each row contains two integers:
 
-        + Rule ID, the number between 0 and :math:`nAntecedentItems-1`.
-        + Item ID, the number between 0 and :math:`nUniqueItems-1`.
+       + Rule ID, the number between 0 and :math:`nAntecedentItems-1`.
+       + Item ID, the number between 0 and :math:`nUniqueItems-1`.
 
    * - ``conseqentItemsets``
      - Pointer to the :math:`nConsequentItems \times 2` numeric table that contains the
        right-hand-side (Y) part of the association rules. Each row contains two integers:
 
-        + Rule ID, the number between 0 and :math:`nConsequentItems-1`.
-        + Item ID, the number between 0 and :math:`nUniqueItems-1`.
+       + Rule ID, the number between 0 and :math:`nConsequentItems-1`.
+       + Item ID, the number between 0 and :math:`nUniqueItems-1`.
 
    * - ``confidence``
      - Pointer to the :math:`nRules \times 1` numeric table that contains confidence values
@@ -224,7 +232,7 @@ Examples
     - :cpp_example:`assoc_rules_apriori_batch.cpp <association_rules/assoc_rules_apriori_batch.cpp>`
 
   .. tab:: Java*
-  
+
     .. note:: There is no support for Java on GPU.
 
     Batch Processing:
@@ -234,7 +242,7 @@ Examples
   .. tab:: Python*
 
     Batch Processing:
-    
+
     - :daal4py_example:`association_rules_batch.py`
 
 Performance Considerations