👂🎴 🕸️
Experiential
learning
''
Unsupervised
learning
''
Supervised
learning
''
Classifiers
&
Machine
Learning
...
<
div
>“
It
is
unworthy
of
excellent
men
to
lose
hours
like
slaves
in
the
labour
of
calculation
which
could
safely
be
relegated
to
anyone
else
if
machines
were
used
."
div
><
div
><
br
>
div
><
div
>
G
.
W
.
Leibniz
(
Describing
''
in
1685
''
the
value
to
astronomers
of
the
hand
-
cranked
calculating
machine
he
had
invented
in
1673
.)
div
><
div
><
br
>
div
>
In
machines
''
reinforcement
learning
(
RL
)
is
implemented
using
an
agent
-
environment
framework
.
The
agent
interacts
with
an
environment
by
taking
actions
based
on
a
policy
(
a
strategy
for
decision
-
making
).
The
environment
provides
feedback
in
the
form
of
rewards
or
penalties
''
guiding
the
agent
to
improve
its
actions
.
Key
components
include
a
reward
function
to
evaluate
outcomes
''
a
value
function
to
estimate
long
-
term
benefits
of
actions
''
and
exploration
strategies
to
balance
learning
new
behaviors
versus
exploiting
known
rewards
.
Supervised
learning
resembles
a
structured
classroom
environment
''
where
explicit
feedback
is
given
for
each
example
(
e
.
g
.''
a
teacher
correcting
a
student
'
s
answers
).
In
contrast
''
reinforcement
learning
mirrors
experiential
learning
''
where
feedback
comes
as
rewards
or
penalties
after
actions
''
guiding
behavior
toward
long
-
term
goals
.
For
instance
''
a
child
learning
to
ride
a
bike
might
fall
(
penalty
)
or
stay
balanced
(
reward
)''
gradually
improving
through
trial
and
error
.
<
div
>
When
satisfaction
follows
association
''
it
is
more
likely
to
be
repeated
.<
br
>
div
>
<
p
>
Q
-
learning
is
a
model
-
free
reinforcement
learning
algorithm
that
enables
an
agent
to
learn
an
optimal
policy
for
decision
-
making
.
It
works
by
estimating
the
<
strong
>
Q
-
values
strong
>
(
action
-
value
function
)''
which
represent
the
expected
cumulative
reward
for
taking
an
action
in
a
given
state
and
following
the
best
future
actions
.
The
agent
updates
Q
-
values
iteratively
using
the
formula
:
p
><
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src
=
https
://
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.
medium
.
com
/
v2
/
resize
:
fit
:
1043
/
1
*
vTMQI14ls9lWzRXzJGi4sg
.
jpeg
/>
DRL
is
a
type
of
machine
learning
where
an
agent
learns
to
make
decisions
by
trial
and
error
''
guided
by
rewards
or
penalties
''
using
deep
neural
networks
.
Unlike
traditional
methods
''
which
struggle
with
complex
environments
''
DRL
allows
machines
to
learn
directly
from
raw
data
''
like
images
or
game
screens
.
The
neural
network
helps
the
agent
recognize
patterns
and
improve
its
decisions
over
time
.
DRL
has
achieved
impressive
results
in
tasks
like
playing
video
games
(
e
.
g
.''
Atari
''
AlphaGo
)''
controlling
robots
''
and
developing
self
-
driving
cars
''
making
it
a
powerful
tool
for
solving
real
-
world
problems
involving
sequential
decision
-
making
In
2016
''
AlphaGo
stunned
the
world
by
defeating
Go
champion
Lee
Sedol
''
proving
that
AI
could
outthink
humans
in
one
of
the
most
complex
games
ever
.
Using
deep
learning
and
Monte
Carlo
Tree
Search
''
it
played
moves
no
human
dared
showcasing
creativity
''
brilliance
''
and
the
unsettling
realization
that
humanity
might
be
screwed
.<
br
>
Cells
that
fire
together
''
wire
together
.
When
two
neurons
in
the
brain
activate
at
the
same
time
repeatedly
''
their
connection
strengthens
.
This
makes
it
easier
and
<
strong
>
more
probable
strong
>
for
one
to
trigger
the
other
in
the
future
.
Imagine
practicing
a
particular
brushstroke
over
and
over
.
Each
time
''
your
hand
and
brain
coordinate
''
and
with
practice
''
the
connection
becomes
stronger
and
the
stroke
becomes
smoother
.
Similarly
''
Hebb
s
law
underpins
how
practice
makes
perfect
.
<
div
>
Es
begab
sich
aber
zu
der
Zeit
''
daß
ein
Gebot
von
dem
Kaiser
Augustus
ausging
''
daß
alle
Welt
geschätzt
würde
.
div
><
div
><
br
>
div
><
div
>
Und
diese
Schätzung
war
die
allererste
und
geschah
zu
der
Zeit
''
da
Cyrenius
Landpfleger
von
Syrien
war
.
div
><
div
><
br
>
div
><
div
>
Und
jedermann
ging
''
daß
er
sich
schätzen
ließe
''
ein
jeglicher
in
seine
Stadt
.
div
><
div
><
br
>
div
><
div
>
Da
machte
sich
auch
auf
Joseph
aus
Galiläa
''
aus
der
Stadt
Nazareth
''
in
das
jüdische
Land
zur
Stadt
Davids
''
die
da
heißt
Bethlehem
''
darum
daß
er
von
dem
Hause
und
Geschlechte
Davids
war
''
auf
daß
er
sich
schätzen
ließe
mit
Maria
''
seinem
vertrauten
Weibe
''
die
ward
schwanger
...
div
>
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