Currently, Keras and Tensorflow seem the best choice to run deep learning model in R.
However, it does not integrate with R very well like dplyr and sparklyr.
tf$xxx style to call tensorflow function does not fit R programming philosophy,
tf seems like too redundant.
From my point of view, dplyr lazy evaluation can propagate to more scenarios which support DAG mode or preview mode with
df %>% head() operation.
Recently, I found a gluon package on mxnet, which can convert pipeline numeric to symbolic just like dplyr using tbl(sc,"some_table_in_database") %>% collect.
However, dplyr is the greatest interface design that I have ever seen before even gluon.hybridize().
Is anyone interested in this topic?