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Contents
• 기계학습이란?
• 신경망이란
• 인공지능 SW TensorFlow
• 딥러닝실습
• 딥러닝응용
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 Chungan Univercity
Mario [email protected]
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.”
Today’s information
* http://www.cray.com/Assets/Images/urika/edge/analytics-infographic.html
Google (2)
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.
What is Artificial Intelligence?
Artificial IntelligenceUnderstand information, To Learn, To Reason, & Act upon it
Object Recognition
Make predictions on data
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
Traditional learning vs Deep Machine Learning
Eiffel Tower
Eiffel Tower
RAW data
RAW data
Deep Learning Network
FeatureExtraction
Vectored Classification
Traditional Learning
Deep Learning
What is a neural network?
Yes/No(Mug or not?)
Data (image)
!
x1 ∈!5 , !x2∈!5
x2 = (W1 × x1)+x3 = (W2 × x2)+
x1 x2 x3x4
x5
W4W3W2W1
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
Convolution Feature
Why is Deep Learning taking off?
Engine
Fuel
Large neural networks
Labeled data (x,y pairs)
Training Process
Deep learning - CNN
Deep learning : CNN
The Big Players
Open Source Software for Machine Learning
Caffe
Theano
Convnet.js
Torch7
Chainer
DL4J
TensorFlow
Neon
SANOA
Summingbird
Apache SA
Flink ML
Mahout
Spark MLlib
RapidMiner
Weka
Knife
Scikit-learn
Amazon ML
BigML
DataRobot
FICO
Google prediction API
HPE haven OnDemand
IBM Watson
PurePredictive
Yottamine
Deep Learning
StreamAnalytics
Big DataMachine Learning
Data Mining
Machine Learning As a Service
Pylearn2
Google Tensorflow
* Source: Oriol Vinyals – Research Scientist at Google Brain
Expressing High-Level ML Computations
• Core in C++ • Different front ends for specifying/driving the computation
• Python and C++ today, easy to add more
* Source: Jeff Dean– Research Scientist at Google Brain
Hello World on TensorFlow
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 code
MNIST
Old Character Recognition
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 ?
Language Generating
* Source: Oriol Vinyals – Research Scientist at Google Brain
Image Caption Generation
Neural Conversational Model
Neuro Painter
Deep Art
Inceptionism
Automatic Colorization of Black and White Images
Image Generate
Image Segmentation
Scene Parsing
[Farabet et al. ICML 2012, PAMI 2013]
Scene Parsing
[Farabet et al. ICML 2012, PAMI 2013]
Auto pilot car
Q-Learning
How do data science techniques scale with amount of data?
GPU
Inspirer Humanity
Thanks you!
Q&A
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