<s>
NNPDF	B-Algorithm
is	O
the	O
acronym	O
used	O
to	O
identify	O
the	O
parton	O
distribution	O
functions	O
from	O
the	O
NNPDF	B-Algorithm
Collaboration	O
.	O
</s>
<s>
neural	B-Architecture
networks	I-Architecture
as	O
basic	O
interpolating	O
functions	O
.	O
</s>
<s>
The	O
NNPDF	B-Algorithm
approach	O
can	O
be	O
divided	O
into	O
four	O
main	O
steps	O
:	O
</s>
<s>
The	O
training	O
(	O
minimization	O
of	O
the	O
)	O
of	O
a	O
set	O
of	O
PDFs	O
parametrized	O
by	O
neural	B-Architecture
networks	I-Architecture
on	O
each	O
of	O
the	O
above	O
MC	O
replicas	O
of	O
the	O
data	O
.	O
</s>
<s>
Since	O
the	O
PDF	O
parametrization	O
is	O
redundant	O
,	O
the	O
minimization	O
strategy	O
is	O
based	O
in	O
genetic	B-Algorithm
algorithms	I-Algorithm
as	O
well	O
as	O
gradient	O
descent	O
based	O
minimizers	O
.	O
</s>
<s>
The	O
neural	B-Architecture
network	I-Architecture
training	O
is	O
stopped	O
dynamically	O
before	O
entering	O
into	O
the	O
overlearning	O
regime	O
,	O
that	O
is	O
,	O
so	O
that	O
the	O
PDFs	O
learn	O
the	O
physical	O
laws	O
which	O
underlie	O
experimental	O
data	O
without	O
fitting	O
simultaneously	O
statistical	O
noise	O
.	O
</s>
<s>
The	O
set	O
of	O
PDF	O
sets	O
(	O
trained	O
neural	B-Architecture
networks	I-Architecture
)	O
provides	O
a	O
representation	O
of	O
the	O
underlying	O
PDF	O
probability	O
density	O
,	O
from	O
which	O
any	O
statistical	O
estimator	O
can	O
be	O
computed	O
.	O
</s>
<s>
The	O
NNPDF	B-Algorithm
releases	O
are	O
summarised	O
in	O
the	O
following	O
table	O
:	O
</s>
