  Although database systems perform well in data access and manipulation, their
relational model hinders data scientists from formulating machine learning
algorithms in SQL. Nevertheless, we argue that modern database systems perform
well for machine learning algorithms expressed in relational algebra. To
overcome the barrier of the relational model, this paper shows how to transform
data into a relational representation for training neural networks in SQL: We
first describe building blocks for data transformation, model training and
inference in SQL-92 and their counterparts using an extended array data type.
Then, we compare the implementation for model training and inference using
array data types to the one using a relational representation in SQL-92 only.
The evaluation in terms of runtime and memory consumption proves the
suitability of modern database systems for matrix algebra, although specialised
array data types perform better than matrices in relational representation.
