Embed Size (px)
Citation preview
Contents
• 기계학습 (NNs)
• Neural Networks
• 딥러닝기술
• 활용방안
Mario ChoDevelopment Experience◆ Image Recognition using Neural Network◆ Bio-Medical Data Processing◆ Human Brain Mapping on High Performance
Computing◆ Medical Image Reconstruction
(Computer Tomography) ◆Enterprise System◆Open Source Software Developer
Open Source Software Developer◆ OPNFV (NFV&SDN) & OpenStack◆ Machine Learning (TensorFlow)
Book◆ Unix V6 Kernel Korea Open Source Software Lab.
Mario [email protected]
Today’s information
* http://www.cray.com/Assets/Images/urika/edge/analytics-infographic.html
The Future of Jobs
“The Fourth Industrial Revolution, which includes developments in previously disjointed fields such as artificial intelligence & machine-learning, robotics, nanotechnology, 3-D printing, and genetics & biotechnology, will cause widespread disruption not only to business models but also to labor market over the next five years, with enormous change predicted in the skill sets needed to thrive in the new landscape.”
What is the Machine Learning ?• Field of Computer Science that evolved from the study of pattern recognition and computational learning theory into Artificial Intelligence.
• Its goal is to give computers the ability to learn without being explicitly programmed.
• For this purpose, Machine Learning uses mathematical / statistical techniques to construct models from a set of observed data rather than have specific set of instructions entered by the user that define the model for that set of data.
Required of New type of Computing understand information, to learn, to reason, and act upon it
Traditional learning vs Deep Machine Learning
Eiffel Tower
Eiffel Tower
RAW data
RAW data
Deep Learning Network
FeatureExtraction
Vectored Classification
Traditional Learning
Deep Learning
Neuron
Human Intelligence
Brain Map
hippocampus
Neural network vs Learning networkNeural Network Deep Learning Network
Neural Network
W1
W2
W3
f(x)
1.4
-2.5
-0.06
Neural Network
2.7
-8.6
0.002
f(x)
1.4
-2.5
-0.06
x = -0.06×2.7 + 2.5×8.6 + 1.4×0.002 = 21.34
Neural Network : x1 XNOR x2
+1
x1
x2
+1
a1(2)
a2(2)
a1(3)
-30
20
20
10
-20
-10
-1010
-20x1 x2 a1(2) a2(2) a1(3)
0 0 0 1 10 1 0 0 01 0 0 0 01 1 1 0 0
Output10
Multi-layer Neural Networks
Train this layer first
Multi-layer Neural Networks
Train this layer firstthen this layer
then this layerthen this layer
finally this layer
Tic Tac Toc
AlphaGo
RAW data
Layer1Policy network & value network
Selection
Expansions
Evaluation
Backup
~ Layer13
Deep learning - CNN
Image recognition in Google Map
* Source: Oriol Vinyals – Research Scientist at Google Brain
Deep Learning Hello World == MNIST
MNIST (predict number of image)
CNN (convolution neural network) training
MNIST
Old Character Recognition
Human-Level Object Recognition
• ImageNet• Large-Scale Visual Recognition Challenge�Image Classification / Localization�1.2M labeled images, 1000 classes�Convolutional Neural Networks (CNNs)has been dominating the contest since..� 2012 non-CNN: 26.2% (top-5 error)� 2012: (Hinton, AlexNet)15.3%� 2013: (Clarifai) 11.2%� 2014: (Google, GoogLeNet) 6.7%� 2015: (Google) 4.9%� Beyond human-level performance
Hierarchical Representation of Deep Learning
* Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks”.
* Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks”.
Deep Learning (object parts)
Face extraction method
Face recognition data- sets?
Human-Level Face Recognition
• Convolutional neural networks based face recognition system is dominant
• 99.15% face verification accuracy on LFW dataset in DeepID2 (2014)� Beyond human-level recognition
Source: Taigman et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR’14
Image Recognition
* Source: Oriol Vinyals – Research Scientist at Google Brain
Object Classification and Detection
How to the Object recognition ?
Image Caption Generation
Language Generating
* Source: Oriol Vinyals – Research Scientist at Google Brain
Scene Parsing
[Farabet et al. ICML 2012, PAMI 2013]
Scene Parsing
[Farabet et al. ICML 2012, PAMI 2013]
Auto pilot car
Automatic Colorization of Black and White Images
Generate sounds on old-movies
Automatic Machine Translation• Automatic Translation of Text.
• Automatic Translation of Images.
Automatic Handwriting Generation
Text Generation
create stylized images from rough sketches.
Inspirer Humanity
Thanks you!
Q&A
