Deep Learning vs. Machine Learning: Key Differences
Digital
technologies
such
as
Deep
Learning
and
Machine
Learning
have
taken
the
world
by
storm.
Although
these
technologies
are
in
their
nascent
stage,
they
have
shown
immense
potential.
The
emergence
of
these
technological
concepts
is
a
boon
for
individuals
and
businesses
alike.
Today,
it
has
become
a
necessity
to
gain
insight
into
the
technologies
that
are
revolutionizing
our
lives.
However,
if
you
want
to
understand
these
technologies
at
a
comprehensive
level,
you
need
to
familiarize
yourself
with
not
only
the
unique
concepts
but
also
the
differences
that
exist
between
them.
For
someone
who
wants
to
have
a
promising
career
in
the
competitive
IT
industry,
it
is
essential
to
know
how
concepts
such
as
DL
and
ML
are
influencing
the
trajectory
of
AI.
Before
understanding
the
deep
learning
vs
machine
learning
difference,
it
is
essential
to
have
a
clear
knowledge
of
the
respective
technological
concepts.
What
is
Machine
Learning?
Machine
Learning
(ML)
is
a
subdomain
of
Artificial
Intelligence
technology.
ML
primarily
focuses
on
the
creation
of
algorithms
and
statistical
models
that
allow
computer
systems
to
learn.
Thanks
to
ML,
computers
can
make
decisions
or
predictions
without the
need to
explicitly
program
them.
Thus,
Machine
Learning
involves
training
algorithms
to
support
the
identification
of
relationships
and
patterns.
Ultimately,
such
patterns
help
make
predictions
about
new
data.
In
simple
words,
ML
is
the
type
of
Artificial
Intelligence
that
relies
on
minimum
human
interference.
Some
of
the
chief
features
of
Machine
Learning
are:
-
ML
algorithms
enable
the
automation
of
processes,
which
help
in
locating
patterns
in
data. -
Machine
Learning
techniques
are
ideal
for
processing
big
data
as
they
can
handle
a
substantial
volume
of
data. -
Innovative
technology
can
help
to
make
predictions
about
future
events. -
ML
algorithms
are
capable
of
discovering
wide
patterns
in
data
that
can
help
analyze
unexplored
and
fresh
data.
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What
is
Deep
Learning?
Deep
Learning
is
a
part
of
Machine
Learning.
It
makes
use
of
neural
networks
with
diverse
layers
to
analyze
complex
associations
and
patterns
in
data.
It
is
capable
of
mimicking
the
human
brain
to
carry
out
a
broad
range
of
tasks,
such
as
speech
recognition
as
well
as
natural
language
processing.
One
of
the
main
highlights
of
Deep
Learning
models
is
that
they
are
capable
of
learning.
Hence,
they
can
improve
over
time,
and
their
level
of
accuracy
can
improve
as
they
process
data.
Some
of
the
chief
features
of
Deep
Learning
are:
-
The
Deep Learning
technology
works
by
mimicking
the
human
brain. -
The
deep
neural
networks
that
makeup
DL
have
numerous
layers
that
support
the
model
in
learning
the
data’s
hierarchical
representation. -
The
features
of
DL
relating
to
high
power
and
flexibility
make
it
suitable
for
varying
tasks
like
speech
and
image
recognition,
etc. -
Deep
Learning
algorithms
are
highly
helpful
when
there
are a
large
number
of inputs as
well
as outputs.
Although
to
a
layman,
the
terms
Artificial
Intelligence,
Machine
Learning,
and
Deep
Learning
may
sound
similar.
The
reality
is
that
each
of
these
concepts
is
different
from
one
another.
However,
they
are
related
to
each
other.
Now
that
you
have
clarity
on
deep
learning
and
machine
learning
concepts,
it
is
time
to
answer
the
following
question: What
are
the
differences
between
deep
learning
and
machine
learning?
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Difference
between
Deep
Learning
and
Machine
Learning
There
exist
a
number
of
deep
learning
vs
machine
learning
differences.
By
identifying
how
the
technological
concepts
are
unique,
it
is
possible
to
identify
their
true
potential.
-
Conceptual
difference
Machine
Learning
is
the
branch
of
Artificial
Intelligence.
Since
it
is
trained
on
statistical
models
and
algorithms,
it
is
capable
of
making
predictions.
The
technology
relies
on
historical
data
as
well
as
training to
strengthen its
capabilities.
A
key
thing
to
bear
in
mind
is
that
ML
relies
on
human
intervention
so
that
data
can
be
fed
and
improvements
can
be
made.
Deep
Learning
can
be
defined
as
the
subset
of
Machine
Learning
technology.
It
is
a
model
that
can
help
an
algorithm
ascertain
whether
a
prediction
is
accurate
or
not.
The
role
of
neural
networks
is
indispensable
in
Deep
Learning.
As
neural
networks
empower
software
to
comprehend
complex
patterns,
there
is
no
need
for
human
intervention.
-
Data
Representation
The difference
between
machine
learning
and
deep
learning arises
from
the
use
of
data
representation.
In
the
case
of
machine
learning,
the
data
representation
that
is
used
is
quite
different
from
that
of
deep
learning.
In
Machine
Learning,
structured
data
is
used.
This
type
of
data
is
highly
valuable
since
it
can
be
searched
and
analyzed in
a
simple
and
efficient
manner.
ML
techniques
may
find
it
challenging
to
work
on
unstructured
data.
This
is due
to
the
fact
that the
training
dataset
may
have
infinite
variations.
The
data
representation
that
is
used
in
Deep
Learning
is
different
as
it
involves
neural
networks.
DL
is
capable
of
working
with
not
only
structured
data
but
also
with
unstructured
data.
The
technology
can
be
used
for
a
broad
range
of
unstructured
data
such
as
images,
audio,
text
as
well
as
tables
containing
data.
-
Data
points
The
values
that
are
arranged
in
structures
are
known
as
data
points.
Machine
Learning
encompasses
thousands
of
data
points,
whereas
Deep
Learning
involves
millions
of
data
points.
This
feature
goes
on
to
show
that
Machine
Learning
is
capable
of
working
well
with relatively
small
databases.
On
the
other
hand,
Deep
Learning
relies
on
a
humongous
volume
of
data
while
carrying
out
its
task.
DL basically requires
large
amounts
of
data
so
that
it
can
understand
and
perform
tasks
better
than
conventional
ML
algorithms.
-
Training
time
While
exploring deep
learning
vs
machine
learning,
a
key
difference
that
arises
is
related
to
the
training
time.
Training
time
is basically the
time
that
is
taken
by
a
model
for the
purpose
of training
on
a
dataset.
When
it
comes
to
ML
algorithms,
they
require
less
time
for
training.
The
training
time
may
range
between
a
few
seconds
to a
number
of hours.
However,
the
same
is
not
the
case
when
it
comes
to
Deep
Learning.
The
training
time
for
a
Deep
Learning
neural
network
may
span
between
a
few
hours
to
several
weeks.
However,
it
may
be
possible
to
adopt
certain
methods
to
hasten
the
training
time.
-
Effectiveness
and
setting
up
time
When
it
comes
to
Machine
Learning,
it
may
be
possible
to
set
up
and
run
the
system
in
no
time.
However,
although
it
may
be
easy
to
set
up
the
system,
it
does
not
mean
that
its
effectiveness
is
guaranteed. In
fact, the
effectiveness
of
the
system
may
be
hampered.
On
the
contrary,
setting
up
Deep
Learning
may
seem
time-consuming,
but
there
is
no
compromise
on
the
effectiveness
of
the
system.
Once
the
system
is
up
and
running,
there
is
a
need
for
limited
or
no
intervention.
It
is
capable
of
generating
results in
an
effective
manner.
Moreover,
the
quality
of
the
outcome
is
likely
to
get
better with
the
passage
of
time.
-
Need
for
resources
The
resources
that
are
required
for
ML
systems
and
DL
systems
may
vary
considerably.
It
is
possible
to
execute
Machine
Learning
on
simple
and
standard
computer
systems.
As
ML
does
not
require
a
large
volume
of
computational
power,
it
can
be
supported
by
a
standard
computer.
However,
the
same
cannot
be
said
about
deep
Learning.
Deep
Learning
systems
require
powerful
hardware
and
software
resources
so
that
they
can
function
properly.
As
DL
involves
a
broad
number
of
operations,
it
needs
a
robust
system
that
can
support
it.
In
the
absence
of
appropriate
resources,
the
efficiency
and
functionality
of
DL
may be diminished.
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Featurization
process
When
it
comes
to
deep
learning
vs
machine
learning,
a
key
difference
is
related
to
the
featurization
process.
In
machine
learning,
it
is
essential
for
users
to
identify
and
create
features
accurately.
It
shows
that
the
technology
relies
on
users
to
carry
out
its
tasks
and
functions.
However,
in
the
case
of
deep
learning
technology,
there
is
no
need
for
users
to
intervene.
DL
is
capable
of
learning
high-level
features
from
the
data
itself.
That’s
not
all!
Deep
Learning
can
create
new
features
all
by
itself
without
relying
on
the
help
and
support
of
humans.
The
table
below
presents
a
comparison
between
Deep
Learning
and
Machine
Learning.
<pre>Please include attribution to 101blockchains.com with this graphic. <a href='https://101blockchains.com/blockchain-infographics/'> <img data-src='https://www.dashgeneration.com/wp-content/uploads/2024/05/deep-learning-vs-machine-learning-key-differences-2.png' alt='deep learning vs machine learning='0' /> </a>
Future
of
ML
and
DL
In
the
technology-driven
era,
the
future
of
Machine
Learning
and
Deep
Learning
is
full
of
promise
and
potential.
The
unique
features
and
capabilities
of
these
technologies
make
them
highly
capable
of
carrying
out
a
broad
range
of
tasks
and
operations.
As
AI
technology
is
reaching
new
heights,
the
future
of
both
ML
and
DL
is
extremely
bright.
If
you
wish
to
make
your
career
in
the
dynamic
IT
domain,
it
is
a
must
to
broaden
your
knowledge
about
AI,
ML,
and
DL.
Currently,
these
technologies
are
serving
as
catalysts
and
influencing
the
trajectory
of
the
IT
industry.
The
emergence
of
technologies
such
as
Machine
Learning
and
Deep
Learning
have
given
rise
to
new
job
opportunities.
If
you
are
passionate
about
establishing
yourself
as
a
competent
IT
professional,
you
need
to
brush
up on
your skills
relating
to
ML
and
DL.
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Conclusion
In
the
Artificial
Intelligence
landscape,
technologies
such
as
Machine
Learning
and
Deep
Learning
play
a
key
role.
The
key
differences
between
ML
and
DL
have
been
presented.
The
technologies
give
a
glimpse
into
the
power
of
modern
technologies.
There
exist
a
number
of
deep
learning
vs
machine
learning
differences
relating
to
the
underlying
concepts,
data
representation,
use
of
data
points,
training
time,
effectiveness,
and
setting
up
time
resource
requirements
and
featurization
processes.
Today,
both
machine
learning
and
deep
learning
are
revolutionary
technologies.
The
emergence
of
these
technologies
has
been
of
immense
value.
The
future
of
these
technological
concepts
is
full
of
high
potential.
These
modern
technologies
have
given
rise
to
new
opportunities
and
possibilities
in
the
vast
IT
industry.
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