Slides credited from Prof. Hung-Yi Lee

Slides credited from Prof. Hung-Yi Lee

What is Machine Learning?

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What Computers Can Do?

Programs can do the things you ask them to do

3

Program for Solving Tasks

Task: predicting positive or negative given a product review

“I love this product!” “It claims too much.” “It’s a little expensive.”

+ - ?

“台灣第一波上市!” “規格好雞肋…” “樓下買了我才考慮”

推 ^噓

?

program.py

Some tasks are complex, and we don’t know how to write a program to solve them.

if input contains “love”, “like”, etc.

output = positive

if input contains “too much”, “bad”, etc.

output = negative

program.py program.py

program.py program.py program.py

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Learning ≈ Looking for a Function

Task: predicting positive or negative given a product review

“I love this product!” “It claims too much.” “It’s a little expensive.”

+ - ?

“台灣第一波上市!” “規格好雞肋…” “樓下買了我才考慮”

推 ^噓

?

f f f

f f f

Given a large amount of data, the machine learns what the function f should be.

5

Learning ≈ Looking for a Function

Speech Recognition

Handwritten Recognition

Weather forecast

Play video games

( ) ⁼

f

( ) ⁼

f

( ) ⁼

f

( ) ⁼

f

“2”

“你好”

“ Saturday”

“move left”

Thursday

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Machine Learning Framework

Training is to pick the best function given the observed data Testing is to predict the label using the learned function Training Data

Model: Hypothesis Function Set

2

1, f f

Training: Pick the best function f ^*

Testing: ^{f }

( )

^{ =}

^{ y  = +}

f

“Best” Function

( ) ( )





Testing Data

( )





“It claims too much.”

-

: x

ˆy :

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function input function output

What is Deep Learning?

A subfield of machine learning

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Stacked Functions Learned by Machine

Production line (生產線)

“台灣第一波

上市!” 推

End-to-end training: what each function should do is learned automatically

Simple Function

f₁

Simple Function

f₂

Simple Function

f₃

Deep Learning Model

f: a very complex function

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Deep learning usually refers to neural network based model

Stacked Functions Learned by Machine

Output Layer Hidden Layers

Input Layer

Layer 1 Layer 2 Layer L

Input Output

vector x

“台灣第一波 上市!”

label y 推 x

x

……

x

…… …… ……

……

……

……

y

Features / Representations

Representation Learning attempts to learn good features/representations

Deep Learning attempts to learn (multiple levels of) representations and an output

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Deep v.s. Shallow – Speech Recognition

Shallow Model

MFCC Waveform

…

Filter bank DFT

DCT log

GMM

spectrogram

“Hello”

:hand-crafted :learned from data Each box is a simple function in the production line:

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Deep v.s. Shallow – Speech Recognition

Deep Model

All functions are learned from data

Less engineering labor, but machine learns more f

“Hello”

f

f

f

f

“Bye bye, MFCC” - Deng Li in Interspeech 2014 12

Deep v.s. Shallow – Image Recognition

Shallow Model

:hand-crafted :learned from data

http://www.robots.ox.ac.uk/~vgg/research/encoding_eval/ 13

Deep v.s. Shallow – Image Recognition

Deep Model

Reference: Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In Computer Vision–ECCV 2014 (pp. 818-833)

“monkey”

f

f

f

f

All functions are learned from data

Features / Representations

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Machine Learning v.s. Deep Learning

Machine Learning

describing your data with features a

computer can understand

model learning algorithm

Credit by Dr. Socher

hand-crafted domain-specific knowledge

optimizing the weights on features

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Machine Learning v.s. Deep Learning

Deep Learning

representations learned by machine

model learning algorithm

automatically learned internal knowledge

optimizing the weights on features

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Deep learning usually refers to neural network based model

Inspired by Human Brain

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A Single Neuron

z w

w

w

…

x

x

x

+ b

( ) ^z

 ^ ( ) ^z

z

bias

y

( )

z e

= + 1

 1

Sigmoid function Activation

function

Each neuron is a very simple function

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Deep Neural Network

Cascading the neurons to form a neural network

x

x

……

Layer 1

……

y

y

……

Layer 2

……

Layer L

……

……

……

Input Output

y

x

M

N

R

R

f : →

A neural network is a complex function:

Each layer is a simple function in the production line

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History of Deep Learning

1960s: Perceptron (single layer neural network) 1969: Perceptron has limitation

1980s: Multi-layer perceptron 1986: Backpropagation

1989: 1 hidden layer is “good enough”, why deep?

2006: RBM initialization (breakthrough) 2009: GPU

2010: breakthrough in Speech Recognition (Dahl et al., 2010) 2012: breakthrough in ImageNet (Krizhevsky et al. 2012)

2015: “superhuman” results in Image and Speech Recognition

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Deep Learning Breakthrough

First: Speech Recognition

Second: Computer Vision

21 Acoustic Model WER on RT03S FSH WER on Hub5 SWB

Traditional Features 27.4% 23.6%

Deep Learning 18.5% (-33%) 16.1% (-32%)

History of Deep Learning

1960s: Perceptron (single layer neural network) 1969: Perceptron has limitation

1980s: Multi-layer perceptron 1986: Backpropagation

1989: 1 hidden layer is “good enough”, why deep?

2006: RBM initialization (breakthrough) 2009: GPU

2010: breakthrough in Speech Recognition (Dahl et al., 2010) 2012: breakthrough in ImageNet (Krizhevsky et al. 2012)

2015: “superhuman” results in Image and Speech Recognition

Why does deep learning show breakthrough in applications after 2010?

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Reasons why Deep Learning works

Big Data GPU

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Why to Adopt GPU for Deep Learning?

GPU is like a brain

Human brains create graphical imagination for mental thinking

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台灣好吃牛肉麵

Why Speed Matters?

Training time

◦ Big data increases the training time

◦ Too long training time is not practical

Inference time

◦Users are not patient to wait for the responses

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0 50 100 150 200 250 300

P40 P4 CPU

Time (ms)

GPU enables the real-world applications using the computational power

Why Deeper is Better?

Deeper → More parameters

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x1 x₂

……

……

x1 x₂

……

……

……

Shallow Deep

Universality Theorem

Any continuous function f

can be realized by a network with only hidden layer

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: R R

f

→

http://neuralnetworksanddeeplearning.com/chap4.html

Why “deep” not “fat”?

Fat + Shallow v.s. Thin + Deep

Two networks with the same number of parameters

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x1 x₂

……

……

……

x1 x₂

……

……

Fat + Shallow v.s. Thin + Deep

Hand-Written Digit Classification

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The deeper model uses less parameters to achieve the same performance

Fat + Shallow v.s. Thin + Deep

Two networks with the same number of parameters

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x1 x₂

……

……

……

x1 x₂

……

……

2d d

d

O(2d) O(d

)

How to Apply?

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How to Frame the Learning Problem?

The learning algorithm f is to map the input domain X into the output domain Y

Input domain: word, word sequence, audio signal, click logs Output domain: single label, sequence tags, tree structure, probability distribution

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Y X

f : →

Output Domain – Classification

Sentiment Analysis

Speech Phoneme Recognition Handwritten Recognition

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“這規格有誠意!” +

“太爛了吧~” -

/h/

2

Output Domain – Sequence Prediction

POS Tagging

Speech Recognition

Machine Translation

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“推薦我台大後門的餐廳” 推薦/VV 我/PN 台大/NR 後門/NN

的/DEG 餐廳/NN

“大家好”

“How are you doing today?” “你好嗎?”

Learning tasks are decided by the output domains

Input Domain –

How to Aggregate Information

Input: word sequence, image pixels, audio signal, click logs Property: continuity, temporal, importance distribution Example

◦ CNN (convolutional neural network): local connections, shared weights, pooling

◦ AlexNet, VGGNet, etc.

◦ RNN (recurrent neural network): temporal information

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Network architectures should consider the input domain properties

How to Frame the Learning Problem?

The learning algorithm f is to map the input domain X into the output domain Y

Input domain: word, word sequence, audio signal, click logs Output domain: single label, sequence tags, tree structure, probability distribution

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Y X

f : →

Network design should leverage input and output domain properties

“Applied” Deep Learning

Deep Learning

representations learned by machine

model learning algorithm

automatically learned internal knowledge

optimizing the weights on features

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How to frame a task into a learning problem and design the corresponding model

Core Factors for Applied Deep Learning

1. Data: big data

2. Hardware: GPU computing

3. Talent: design algorithms to allow networks to work for the specific problems

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Concluding Remarks

39

Training

Concluding Remarks

40

Inference

Concluding Remarks

41

Training

Inference

Main focus: how to apply deep learning to the real-world problems

Reference

Reading Materials

◦ Academic papers will be put in the website

Deep Learning

◦ Goodfellow, Bengio, and Courville, “Deep Learning,” 2016.

http://www.deeplearningbook.org

◦ Michael Nielsen, “Neural Networks and Deep Learning”

http://neuralnetworksanddeeplearning.com

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