Boulder Data Science June 9, 2016

1. Intro
to
Neural
Networks
Dean
Wya2e
Boulder
Data
Science
@drwya2e
June
9,
2016

2. Neural
Networks
• AI
summer
is
here!
• In
the
last
year
NNs
have
– ConFnued
SOA
advancements
in
image
and
speech
recogniFon
– Beaten
a
human
player
in
Go
– Provided
some
quanFficaFon
of
“art”

3. About
me

4. • 100,000,000,000
neurons
• 10,000
dendriFc
inputs
per
neuron
• 1
electrical
output
How
does
your
brain
work?

5. One
simple
abstracFon
Dendri'c
input
Synap'c
weights
Soma
Axonal
output

6. Digression
into
regression
• Linear
regression
• LogisFc
regression

7. How
to
learn
the
weights?
• If
we
know
what
output
should
look
like,
can
compute
error
and
update
weights
to
minimize
it
– OpFmizaFon
problem,
typically
use
gradient
descent
_
Correct
output
Output
Error

8. Gradient
descent
• Given
a
cost
funcFon
– MSE
– Cross-­‐entropy
– etc.
• Can
take
step
in
opposite
direcFon
of
cost
gradient
by
compuFng
derivaFve
w.r.t.
weights
• Scale
by
learning
rate
(Fny
step)

9. A
brief
history
of
neural
networks:
The
Perceptron
x1
x2
y
0
0
0
0
1
0
1
0
0
1
1
1
~1960:
“The
perceptron”
Universal
funcFon
approximator
AND

10. A
brief
history
of
neural
networks:
The
Perceptron
~1960:
“The
perceptron”
Universal
funcFon
approximator

11. x1
x2
y
0
0
0
0
1
1
1
0
1
1
1
0
…but
only
if
funcFon
is
linearly
separable
XOR
?
A
brief
history
of
neural
networks:
The
Perceptron

12. • Neural
network
research
halts
(AI
winter)
• Meanwhile…
– Support
Vector
Machine
(SVM)
invented,
solves
non-­‐linear
problems
• Shif
toward
separaFon
of
feature
representaFon
and
classificaFon
– Handcraf
the
best
features,
train
the
SVM
(or
current
state-­‐of-­‐the-­‐
art)
to
do
the
classificaFon
• Eventually,
mulF-­‐layer
perceptron
generalizaFon
realized,
solves
non-­‐linear
problems
– Nobody
cares…
A
brief
history
of
neural
networks:
Next
~30
years
h"ps://www.youtube.com/watch?v=3liCbRZPrZA

13. Handcrafed
arFsanal
features

14. • Discovering
good
features
is
hard!
– Requires
a
lot
of
domain
knowledge
– State
of
the
art
in
computer
vision
was
the
culminaFon
of
years
of
collaboraFon
between
computer
vision
scienFsts,
neuroscienFsts,
etc.
• Neural
networks
automaFcally
learn
features
(weights)
from
examples
based
on
the
task
– Each
neuron
is
a
“feature
detector”
that
acFvates
proporFonately
to
how
well
its
input
matches
its
weights
– Deep
learning:
Shif
back
from
hand-­‐crafed
features
to
features
learned
from
task
General
learning
methods
for
robust
feature
representaFon
and
classificaFon
Hidden
1
Hidden
2
Hidden
3

15. • Handful
of
researchers
sFll
toiling
away
on
neural
networks
with
li2le-­‐to-­‐no
recogniFon
– 2012:
one
grad
student
studying
how
to
implement
neural
networks
on
GPUs
submits
first
“deep
learning”
architecture
to
image
recogniFon
challenge,
wins
by
a
landslide
– 2013:
Almost
every
submission
the
is
a
deep
neural
network
executed
on
GPU
(conFnuing
trend)
A
brief
history
of
neural
networks:
Deep
learning
bandwagon
First
deep
neural
network

16. • 8
layers
• 650,000
“neurons”
(units)
• 60,000,000
learned
parameters
• 630,000,000
connecFons
• Uses
same
basic
algorithm
as
mulF-­‐layer
perceptron
to
learn
weights
• Finally
caught
on
because
– Can
do
it
“fast”
(~1
week
in
2012)
thanks
to
GPU-­‐based
computaFon
– Actually
works
and
with
less
overfikng
due
to
tricks
and
massive
amounts
of
data
AlexNet

17. AlexNet
96
11x11
pixel
filter
weights
learned
from
ImageNet
AlexNet
Handcrafed
Textons
Unseen
image
classificaFons

18. Neural
Networks
in
2016
• Variety
of
libraries
that
specify
inputs
as
tensor
minibatch
and
automaFcally
compute
gradients
– Tensorflow
– Theano
(Keras/Lasagne)
– Torch
• Libraries
also
available
for
common
Neural
Network
layer
types
– ConvoluFonal,
acFvaFon,
pooling,
dropout,
RNN,
etc.
• Almost
too
easy
– Mind
the
danger
zone!

19. Data
science
due
diligence
“Neural
Networks
sound
awesome
and
will
solve
all
our
problems!”
• Significant
investment
in
resources.
GPU
(TPU?)
cluster,
ramp-­‐up
on
niche/rapidly-­‐evolving
tools
• Long
feedback
loop
for
architecture
improvement.
Typically
launch
many
jobs
and
terminate
bad
models
(see
above)
• Need
a
lot
of
high-­‐dimensional
data
with
variability
(millions
of
unique
observaFons
and/or
heavy
data
augmentaFon).
Delicate
balance
of
increased
predicFve
power/overfikng
• Hard
to
debug
when
not
working.
Millions
of
reasons
(literally)
a
model
can
be
wrong,
few
ways
it
can
be
right.
“Black
magic”
• Deep
nonlinear
models
suffer
from
interpretability
issues.
Blackbox
model
(although
acFve
research
here)

21. Thanks
Manuel
Ruder,
Alexey
Dosovitskiy,
Thomas
Brox
(2016).
ArFsFc
style
transfer
for
videos.
h2p://arxiv.org/abs/1604.08610
h2ps://www.youtube.com/watch?v=Khuj4ASldmU

22. Resources

23. “This
is
cool,
but
I
don’t
(want
to)
code”
h2p://playground.tensorflow.org

24. “I
am
comfortable
with
the
SciPy
stack
and
want
to
understand
more”
A
Neural
Network
in
11
lines
of
Python
h2p://iamtrask.github.io/2015/07/12/basic-­‐python-­‐network/

25. “I
am
comfortable
with
ML
libraries
and
want
to
build
a
model”
MNIST
• Keras
h2ps://github.com/fchollet/keras/blob/master/examples/
mnist_cnn.py
• Tensorflow
h2ps://www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/
index.html
Varia'onal
Autoencoders
(also
using
MNIST)
• Keras
h2p://blog.keras.io/building-­‐autoencoders-­‐in-­‐keras.html
• Tensorflow
h2ps://jmetzen.github.io/2015-­‐11-­‐27/vae.html