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A
brief
history
of
deep
learningAlice
GaoLecture10Readings:
this
article
by
Andrew
KurenkovBased
on
work
by
K.
Leyton-Brown,
K.
Larson,
and
P.
van
Beek1/112/11OutlineLearning
GoalsA
brief
historyofdeep
learningLessons
learned
summarizedby
Geoff
Hinton
Revisiting
the
Learning
goals3/11Learning
GoalsBy
the
end
of
the
lecture,
you
should
beable
to?
Describethe
causes
andtheresolutionsof
thetwoAIwintersin
thepast.?
Describethe
fourlessons
learned
summarized
by
Geoff
Hinton.4/11Abrief
history
of
deep
learningA
brief
history
of
deep
learning
based
on
this
article
by
Andrew
Kurenkov.5/11The
birth
of
machine
learning(Frank
Rosenblatt
1957)?
A
perceptron
can
be
used
to
represent
and
learn
logicoperators
(AND,OR,
and
NOT).?
It
was
widely
believed
that
AI
is
solved
if
computers
canperformformallogical
reasoning.6/11First
AI
winter(Marvin
Minsky
1969)?
A
rigorous
analysis
of
the
limitations
of
perceptrons.?
(1)
We
need
to
use
multi-layer
perceptrons
to
representsimple
non-linear
functions
such
as
XOR.?
(2)
No
one
knows
a
good
way
to
train
multi-layer
perceptrons.?
Led
to
the
first
AI
winter
from
the
70s
to
the
80s.7/11The
back-propagation
algorithm(Rumelhart,
Hinton
andWilliams1986)?
In
their
Naturearticle,
theyprecisely
and
concisely
explainedhow
we
can
use
the
back-propagation
algorithm
to
trainmulti-layer
perceptrons.8/11Second
AI
Winter?
Multi-layer
neural
networks
trained
with
back-propagationdon’t
work
well,
especially
compared
to
simpler
models
(e.g.support
vector
machines
and/or
random
forests).?
Led
to
the
second
AI
winter
in
the
mid
90s.9/11The
rise
of
deep
learning?
In
2006,
Hinton
,
Osindero
and
Teh
showed
thatback-propagation
works
if
the
initial
weights
are
chosen
in
asmart
way.?
In
2012,
Hintonentered
the
ImageNet
competition
using
deepconvolutional
neural
networks
anddid
far
better
than
the
nextclosest
entry
(an
error
rate
15.3%
rather
than
26.2%
for
thesecond
best
entry).?
The
deep
learning
tsunami
continues
today.10/11Lessons
learned
summarized
by
Geoff
Hinton?
Our
labeled
data
sets
were
thousands
of
times
too
small.?
Our
computers
were
millions
of
times
too
slow.?
We
initialized
the
weights
in
astupid
way.?
We
used
the
wrong
typeof
non-linearity.11/11Revisiting
the
Learning
GoalsBy
the
end
of
the
lecture,
you
should
beable
to?
Describe
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