  We develop a fully non-invasive use of machine learning in order to enable
open research on Euclid-sized data sets. Our algorithm leaves complete control
over theory and data analysis, unlike many black-box like uses of machine
learning. Focusing on a `3x2 analysis' which combines cosmic shear, galaxy
clustering and tangential shear at a Euclid-like sky coverage, we arrange a
total of 348000 data points into data matrices whose structure permits not only
an easy prediction by neural nets, but it additionally permits the essential
removal from the data of patterns which the neural nets could not `understand'.
The latter provides an often lacking mechanism to control and debias the
inference of physics. The theoretical backbone to our neural net training can
be any conventional (deterministic) theory code, where we chose CLASS. After
training, we infer the seven parameters of a $w$CDM cosmology by Monte Carlo
Markov sampling posteriors at Euclid-like precision within a day. We publicly
provide the neural nets which memorise and output all 3x2 power spectra at a
Euclid-like sky coverage and redshift binning.
