Deep Learning and Design Thinking

Deep Learning
Yen-lung Tsai 
Department of Mathematical Sciences
National Chengchi University
and
Seminar in Information Systems and Applications
Design Thinking
NTHU 2018

!2
Asking a good question
Learning Neural Networks
Applying Design Thinking
Outline

!4
f
What we want to do is just transforming the real
world problem to a function. Then we use deep
learning techniques to ﬁnd the function.

!5
Functions are solution
manuals

!6
I wan to know what
the animal in the
picture is.

!7
Question Answer
Formosan
black bear
Python
We have partial answers

!8
Quesiton Answer
Python
There might be inﬁnitely many possible
cases that we haven’t seen beore
?
Formosan
black bear

!9
Finding our functions by Neural Networks!

!10
“Big 3” of Deep Learning
(Neural Works)
Standard NN CNN RNN

!11
Asking a Question
Seeing an animal in wild, we want to
know what is is?
1

!12
Transforming our
question into a function
f
Formosan
black bear
2

Preparing Data Sets for
Training
( , ) ( , “Python”), , ...
xk+1
, yk+1xk
, yk
x1
, y1 xn
, yn
Training Data Testing Data
3
“Formosan  
black bear”

!14
Reinforcement 
Learning (RL)
Generative Adversarial  
Network (GAN)
VAE
Capsule
Standard NN CNN RNN
Constructing NN4

!15
Once we decide our structure of neural
network, we have a set of parameters need to
adjust.
θ = {wi, bj}
Once we determine the parameters, we have a
function:

!16
Learning
5
The learning is sent to our neural network using
our training data, adjusting our parameters,
and then using a loss function to see how much
diﬀerence we have and the observed value.

!17
Basically, we use the method called:
gradient descent
For Neural Networks, the method is also called:
backpropagation

!18
I want to know the
closing price of a
stock tomorrow.

!19
fDate x
the closing price of
a stock x

!20
f
Use the prices of the
previous week to predict
the price for the next day.
NN, CNN, RNN

!21
I want to know how
may homers a
particular MLB
player can hit in 2018
season.

!22
Player
preformance
in the year of
t-1
[Age, G, PA, AB, R, H, 2B, 3B, HR,
RBI, SB, BB, SO, OPS+, TB]
15 features!
f
Number of
homers in the
year of t
RNN

!25
W1 W2
R1
EOS R1
R2
Rk
EOS
Note that in this mode, each input and output
is not of ﬁxed length!

!26
I want to use AI to
play games (drive a
car, brew coﬀee...)

!27
π
The best
action
CNN + NN

!28
Q Score
Reinforcement Learning
+
Left

!29
some characters
are missing in a
font I like

!30
f
Font A Font B
CNN, VAE, GAN

2
AI Core Technology -
Principles of  
Neural Networks
!31

Remember we just have to
learn a function
!32
f
x1
x2
xn
y1
y2
ym

!33
Three Steps of Learning
Functions
Transfer a real world problem into a function.
Collect training materials that we know "correct
answers."
Find the function!

Black Box Learning
There are really techniques for learning
arbitrary functions
!34

In the 1980-1990 or so, it
is a pretty fancy stuff.
!36

!37
Hidden
Layer
Output
Layer
Input
Layer
Black Box

What is powerful is that neural
networks will learn everything!
And you don't have to tell it what the
function should look like: linear, quadratic
polynomial, and so on.
!38

!40
Fully Connected Neural
Networks
Very popular since 1980s
Standard NN

The action of every neuron
is basically the same!
!42

!43
Each neuron accepts
several inputs and then
sends one output.

!44
Find the weighted
sum of inputs.

!46
Apply the activation
function to the weighted
sum.

!47
Popular activation functions
ReLU Sigmoid Gaussian

!48
Parameters are weights, biases

!49
“Learned” neural network
1
2
1
21 1

!50
Suppose we have the input
21 1
(x1
,x2
) = (1,3)
1
3
1 3
8
1
2
1

!51
Using ReLU as activation functions
1
3
1
2
1

!52
NN needs to be trained
Using “previous exam
questions” to train our
neural network
Learning method is called
backpropagation
Training Methods

When a neural network structure is determined
and activation functions are also determined,
it can be adjusted by weights, biases. We call
the set of these parameters θ, each of which
defines a function, and we treat it as a set.
The Function Space of our NN
!53

We are looking for
Makes the closest to the objective
function
!54

What does “the closest” mean
That means the value of “loss function” is
minimum
!55

Suppose we have the
training data
!56

!57
We the value is as small as possible
The most popular loss
function
What’s
this!?

!58
x 網路路輸出真正的誤差
1 2 3 1
2 5 2 -3
3 6 8 2
4 7 2 -5
5 6 6 0
6 4 8 4
7 8 9 1
How to
calculate total
error?

!59
1 2 3 1
2 5 2 -3
3 6 8 2
4 7 2 -5
5 6 6 0
6 4 8 4
7 8 9 1
Total = 0!!
Can we just
add them up?

!60
1 2 3 1
2 5 2 -3
3 6 8 2
4 7 2 -5
5 6 6 0
6 4 8 4
7 8 9 1
Squared
Sum
1
9
4
25
0
16
1
56 in total

!61
Basically, do such
adjustment
learning rate

What does this
terrible thing mean?
!62

Remember that L is a
function of w1, w2, b1,
!63
The smaller the better

For simplification, we first
think of L has only one
variable w
!64

!65
Current value of w
How to get to
the minimum?

!66
Current value of w
I know, a point to
move to the right!

!67
How does the
computer “see” it?

The slope of the tangent
points to the (local)
maximum direction!
!71

!72
Tangent slope is the
rate of change
Tangent slope
Tangent slope

!73
Pointing (local) maximum
Tangent slope
Tangent slope

!74
Moving toward (local) minimaKey
In order to make the value of the loss
function L smaller, we should adjust the
weight w:

!75
Suppose current w=a, we should update our
w as:

!76
Adjusting the Weight
Tangent slope

!77
Sometimes it
will run over!
Tangent slope

!78
In order not to make too much adjustment
at one time, we will multiply a small
number called Leraning Rate:

!79
However, there are
more than one
parameter

!80
Example
Suppose that
And we are moving toward to local minimum.

!81
Pretend to have only
one parameter!
say, it is w1

!82
All parameters are ﬁxed, except w1
One-variable function!
例⼦

!83
Similarly, we can also deﬁne
Example

!84
So we can adjust w1, w2, b1, and make L
smaller and smaller
w1
w2
b1
Example

!85
Writing together seems more learned!
Updated
We call this the
gradient of L
Example

!86
Partial DerivativeDeﬁnition
What we have done is just so called partial
derivatives.

!87
Similarly,
Partial DerivativeDeﬁnition

!88
gradientNotation
Recall the gradient of L is:

!89
We should have cool notation for the
gradient:
gradientNotation

!90
So we can update all parameters by the
formula:
gradientNotation

This “learning method” has a
fancy name
Gradient Descent
!91

!92
Deep Learning  
Big 3
Standard NN CNN RNN

!94
Convolutional Neural Network
Super star for image
recognition
CNN

!95
Formosan black bear
image recognition
f ( ) =

!96
Playing Video Games
Current states “the best” actionπ ( ) =
reinforcement learning

!97
filter 1
filter 2
input
每個 ﬁlter 看⼀一個特徵, 掃過每
⼀一個點, 紀錄該點附近的「特
徵強度」。
Convolutional Neural Network (CNN)

!99
Say, 3x3 ﬁlters
We need some “filters”

!100
2 5 5 2 5 2 0 1
2 3 4 0 4 2 1 5
4 3 1 3 5 5 4 3
5 3 4 5 0 2 1 5
2 3 1 1 1 0 1 3
4 4 1 1 5 1 1 4
2 3 2 2 0 4 2 4
0 5 4 5 3 4 1 4
35
Think this is an image.
ﬁlter
Dot product
This is learned.
Ｗ＝

!101
2 5 5 2 5 2 0 1
2 3 4 0 4 2 1 5
4 3 1 3 5 5 4 3
5 3 4 5 0 2 1 5
2 3 1 1 1 0 1 3
4 4 1 1 5 1 1 4
2 3 2 2 0 4 2 4
0 5 4 5 3 4 1 4
35 27
ﬁlter
Move to the right
Same matrix
as before!
Ｗ＝

!102
2 5 5 2 5 2 0 1
2 3 4 0 4 2 1 5
4 3 1 3 5 5 4 3
5 3 4 5 0 2 1 5
2 3 1 1 1 0 1 3
4 4 1 1 5 1 1 4
2 3 2 2 0 4 2 4
0 5 4 5 3 4 1 4
35 27 44 32 36 38
36 36 37 36 36 43
37 37 23 26 17 35
29 25 22 18 14 27
27 25 24 21 24 32
31 38 27 34 25 40
ﬁlter
All the way to the end
Ｗ＝

!104
2 5 5 2 5 2 0 1
2 3 4 0 4 2 1 5
4 3 1 3 5 5 4 3
5 3 4 5 0 2 1 5
2 3 1 1 1 0 1 3
4 4 1 1 5 1 1 4
2 3 2 2 0 4 2 4
0 5 4 5 3 4 1 4
35 27 44 32 36 38
36 36 37 36 36 43
37 37 23 26 17 35
29 25 22 18 14 27
27 25 24 21 24 32
31 38 27 34 25 40
ﬁlter
The points on the
picture are one input
layer neuron
Ｗ＝

!105
2 5 5 2 5 2 0 1
2 3 4 0 4 2 1 5
4 3 1 3 5 5 4 3
5 3 4 5 0 2 1 5
2 3 1 1 1 0 1 3
4 4 1 1 5 1 1 4
2 3 2 2 0 4 2 4
0 5 4 5 3 4 1 4
35 27 44 32 36 38
36 36 37 36 36 43
37 37 23 26 17 35
29 25 22 18 14 27
27 25 24 21 24 32
31 38 27 34 25 40
ﬁlter
Conv layers also consist
lots of neural
Ｗ＝

!106
2 5 5 2 5 2 0 1
2 3 4 0 4 2 1 5
4 3 1 3 5 5 4 3
5 3 4 5 0 2 1 5
2 3 1 1 1 0 1 3
4 4 1 1 5 1 1 4
2 3 2 2 0 4 2 4
0 5 4 5 3 4 1 4
35 27 44 32 36 38
36 36 37 36 36 43
37 37 23 26 17 35
29 25 22 18 14 27
27 25 24 21 24 32
31 38 27 34 25 40
ﬁlter
The two layers are not completely connected
Ｗ＝

!107
2 5 5 2 5 2 0 1
2 3 4 0 4 2 1 5
4 3 1 3 5 5 4 3
5 3 4 5 0 2 1 5
2 3 1 1 1 0 1 3
4 4 1 1 5 1 1 4
2 3 2 2 0 4 2 4
0 5 4 5 3 4 1 4
35 27 44 32 36 38
36 36 37 36 36 43
37 37 23 26 17 35
29 25 22 18 14 27
27 25 24 21 24 32
31 38 27 34 25 40
ﬁlter
the weights are shared 
(same as previous ones)
Ｗ＝

!108
35 27 44 32 36 38
36 36 37 36 36 43
37 37 23 26 17 35
29 25 22 18 14 27
27 25 24 21 24 32
31 38 27 34 25 40
Finally, we get a 6x6 matrix,
and actually we usually
make it a 8x8 matrix.
And we have a lot of ﬁlters!

!110
We have to decide the size of area to have one
representative. Say, 2x2.
Basically it is “voting”

!111
35 27 44 32 36 38
36 36 37 36 36 43
37 37 23 26 17 35
29 25 22 18 14 27
27 25 24 21 24 32
31 38 27 34 25 40
36 44 43
37 26 35
38 34 40
“Vote” for the largest number!!

!112
convolution, max-pooling, convolution,  
max-pooling…
We can repeat and repeat

!113
After we finished, we sent
outputs to “normal”
neural networks.

!114
Recurrent Neural Network
Neural Netowork with
memory
RNN

!115
Chatbot
Current word Next wordf ( ) =

!117
W1 W2
R1
EOS R1
R2
Rk
EOS
ChatbotApplication

!118
In fact, the input does not
have to be text, but it is also
possible that the video (one
by one) is output! The
output can still be text, and
the most common is
probably to let the
computer say what
happened in the video.

!119
Translater
Video Captioning Generator
Context Generator
Drawing
Applications

!120
Andrej Karpathy use RNN to generate a book in
“Stacks” (a deep topic in Algebraic Geometry)
http://karpathy.github.io/2015/05/21/rnn-
effectiveness/

!121
PANDARUS:
Alas, I think he shall be come
approached and the day
When little srain would be attain'd into
being never fed,
And who is but a chain and subjects of
his death,
I should not sleep.
Second Senator:
They are away this miseries, produced
upon my soul,
Breaking and strongly should be
buried, when I perish
The earth and thoughts of many states.
“Shakespeare”

!122
Usually, outputs of a Neural
Network are not aﬀected by the
input orders..

!123
However, RNN cells will use previous
outputs as part of inputs

!124
The “unfold” presentation.

!125
Recurrent Neural Network (RNN)
ht
1
= σ (w1
xt
1
+ w2
xt
2
+ w3
h3
t−1
+ w4
h4
t−1
+ b1
)
會加入上⼀一次的
outputs 當這次的
inputs。

!126
Remark
To make it easier for everyone to understand, we
will use simpler representations. Please note
that the inputs are vectors and will have
respected weight; while the outputs are scalars.
xt =
⎡
⎢
⎢
⎢
⎣
xt,1
xt,2
...
xt,n
⎤
⎥
⎥
⎥
⎦
Looks like this

!127
Actually look like this
Remark

!128
h’s work similarly
Remark

!129
Each cell of some
layer, there is one
output.
Inputs could
be vectors
The k-th RNN Cell

!130
The connections of a RNN
layer. Note that RNN cells
will pass their outputs to
other RNN Cells of same
layer.

!131
Formula for the
output of a
standard RNN cell.

!132
LSTM
GRU
Long Short Term Memory
Gated Recurrent Unit
When we say “RNN,” most people
actually think of

!133
LSTM
Long Short Term Memory
The Ace of RNNs

!134
k-th 
LSTM
Cell
Cell status

!136
The output is a number
between 0 and 1
sigmoid
Just decide how big the
“gate” will open

!137
LSTMs has three
types of Gates

!138
forget gate input gate output gate

!142
k-th 
LSTM cell
LSTM Again The cell state is for
the cell only
The output of a cell
will “share” with
other cells

!145
Do we really need to make
things so complicated?

!146
GRU
Gated Recurrent Unit
Simpliﬁed version of LSTM

!147
Although it has “gated” in the name
Only 2 Gates

!149
We could ignore
previous inputs

!151
Key Names of RNNs
Now talking about RNN, in fact,
including the original RNN, LSTM,
GRU and other variants.
In particular, the original RNN is
called Vanilla RNN, and is
SimpleRNN in Keras.

3
Design thinking,
creative problem
solving
!152

!153
Empathize Deﬁne Ideate Prototype Test
Design Thinking Process
(Standford d.school)
Very close to the engineer’s approach

!154
Our model is rarely successful
for the ﬁrst time.
—Yi-Shin Chen, National Tsing Hua University
“ ”

!155
MLB Player Home Runs Prediction

!156
Year t-1 
[Age, G, PA, AB, R, H, 2B, 3B, HR,
RBI, SB, BB, SO, OPS+, TB]
15 features
Number of
homers in the
year of t
f

!157
Use LSTM. Input the data in a period of 10 years and
predict the home runs in the next year.
Only one LSTM layer!

!158
Don't guess the
exact number, guess
which interval!
divided into 5 subintervals:  
0-9, 10-19, 20-29, 30-39, 40+

!159
1
3
2
4
5
10-19
One-Hot encoding
0
1
0
0
0
⎡
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
⎥
⎥
⎥
0-9
10-19
20-29
30-39
40+

!160
Mike Trout (LAA)
Predicted 30-39
Actual 33
Mookie Betts (BOS)
Predicted 20-29
Actual 24
Jose Altuve (HOU)
Predicted 20-29
Actual 24
Kris Bryant (CHC)
Predicted 30-39
Actual 29
Daniel Murphy (WSH)
Predicted 20-29
Actual 23
Corey Seager (LAD)
Predicted 20-29
Actual 22
2017 forecast result

!161
Fancy ways to ask
questions!

!162
VAE
Standard NN CNN RNN
Reinforcement 
Learning (RL)
Generative Adversarial  
Network (GAN)
Capsule

!163
Generative Adversarial
Network
Yann LeCun said GNN is the
most promising model
GAN

!164
There are many interesting recent
development in deep learning…
The most important one, in my opinion, is
adversarial training (also called GAN for
Generative Adversarial Networks).
”
“
—Yan LeCun (楊⽴昆), 2016

!165
The GAN Zoo
https://github.com/hindupuravinash/
the-gan-zoo

!166
generatorNoise
discriminator
Real or Fake
A GAN consists two neural networks,
a Generator and a Discirminator.
z
x
G
D
G(z)

!167
Want
Close to 1
Want
Close to 0
Close to 1
D, G PK!
Discriminator
D
Generator
G

!168
Jun-Yan Zhu et al. (ECCV 2016)
“Generative Visual Manipulation on the Natural Image Manifold”
iGAN
https://arxiv.org/abs/1609.03552

!169
Every one can draw!
https://youtu.be/9c4z6YsBGQ0

!170
Karras et al. NVIDIA team, (ICLR 2018)
“Progressive Growing of GANs for Improved Quality, Stability, and Variation”
Progressive GAN

!171
Karras-Aila-Laine-Lehtinen
By a NVIDIA team
Theano, Python 2, single GPU (Hey, it’s NVIDIA)
Progressive Growing of GANs for Improved
Quality, Stability, and Variation

!172
These are fake (1024x1024)

!173
Isola, Zhu, et al., (CVPR 2017)
“Image-to-Image Translation with Conditional Adversarial Networks”
Pix2Pix

!174
* From the original paper of Isola, Zhu et al. (2017)
Pix2pix transfers satellite images to maps

!175
Pix2pix generates street views
* From the original paper of Isola, Zhu et al. (2017)

!176
* by Christopher Hesse
Pix2pix On-line Version
https://affinelayer.com/pixsrv/

!177
Jun-Yan Zhu et al. (ICCV 2017)
“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”
CycleGAN

!178
G 
Generator
F 
Generator
Domain
A
Domain
B
Discriminator
B
Discriminator
A
CycleGAN

!179
Don’t need to pair
our data sets!!
And open inﬁnitely many possibilities...

!180
Horses to Zebras!!
https://youtu.be/9reHvktowLY

!181
One example from authors of CycleGAN

!182
Face-oﬀ (Tzer-Jen Wei)
https://youtu.be/Fea4kZq0oFQ

!183
AlphaGo key technique

!184
Agent 
(computer)
Environment state
action
reward
rt

!186
π
Left
1 Policy Based
Right
or
State Action
policy function

!187
Usually it is not easy to
learn directly...

!188
Q Grade
+
Action
2 Value Based
(Usually
estimate
reward)
Value function

!189
Netﬂix AlphaGo Film (Highly Recommended)

!192
Fixed one ETF each time
Start with $20,000
Work for one year (Sell all ETF at the end)
Using reinforcement learning
* ETF data provided by the Global
Intelligence

!193
Data from past month
(20 days, in a 20x6
matrix)
f
1
2
3
4
5
Buy 20 units
Buy 10 units
No trading
Sell 10 units
Sell 20 units
5 possible actions

!194
CDQN Hold & buy CDQN Hold & buy
ETF1 17.71% 10.89% ETF11 10.76% 5.26%
ETF2 16.53% 12.6% ETF12 10.19% 13.17%
ETF3 16.3% 0.35% ETF13 7.8% 1.42%
ETF4 14.4% 13.25% ETF14 6.23% 3.56%
ETF5 14.3% 12.7% ETF15 5.73% 4.61%
ETF6 13.91% 13.37% ETF16 3.78% -12.76%
ETF7 13.17% 10.52% ETF17 2.85% 5.83%
ETF8 12.35% 17.07% ETF18 1.59% -4.45%
ETF9 11.68% 10.81% ETF19 1.07% -18.09%
ETF10 11.09% 8.14% ETF20 -0.59% -0.75%
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