Nowadays
,
apparently
text
-
based
image
generators
occupy
the
majority
of
approaches
in
generative
deep
learning
models
.
The
embedding
of
textual
concepts
in
the
space
of
controllable
parameters
of
a
generator
gives
us
the
impression
that
machines
are
starting
to
understand
human
visual
needs
.
It
seems
that
humans
just
need
to
know
how
to
explain
things
to
machines
by
engineering
text
prompts
.
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br
> <
br
>
In
this
course
we
will
not
use
DALL
-
E
,
Midjourney
,
Imagen
and
other
Diffusion
models
,
or
we
can
if
you
want
.
But
it
is
much
more
fun
to
try
to
build
your
own
algorithm
from
scratch
,
which
uses
a
similar
approach
,
and
to
see
what
other
possibilities
it
hides
besides
what
already
exists
in
publicly
released
models
.
Our
aim
is
"
to
open
the
hood
”
and
to
look
inside
the
process
of
“
machine
’
s
comprehension
”
or
maybe
simply
jabber
,
and
to
see
if
the
machine
can
understand
it
.
We
will
use
Python
to
help
the
machine
understand
us
better
.