Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

We consider the problem of how to differentiate In addition to scalability, executing such a code on a relational computations expressed relationally. We show engine has the advantage that the database query experimentally that a relational engine running an optimizer will automatically distribute the computation, taking auto-differentiated relational algorithm can easily into account the sizes of the two matrices. If A and B are scale to very large datasets, and is competitive both large matrices, a database optimizer will consider the with state-of-the-art, special-purpose systems for hardware constraints on each compute node (e.g.