Deep Learning
refer to "Deep Learning Tutorial" by Yann LeCun and others
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Deep Learning
Wikipedia says:
“Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations.”
Machine Learning
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SCALE UP
Totally NEW?
Neural Nets
Perception
RNN
CNN
RBM
DBN
D-AE
AlexNet
GoogLeNet
McCulloch&Pitt 1943
Rosenblatt 1958
Grossberg 1973
Fufushima 1979
Hinton 1999
Hinton 2006
Vincent 2008
Alex 2012
Szegedy 2015
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Learning Representation
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ML/AI: how do we learn features?
- What is the fundamental rule?
- What is the learning algorithms?
- CogSci: how does the mind learn abstract concepts on top of less abstract ones?
- Neuroscience: how does the cortex learn perception?
- Deep learning : addresses the problem of learning hierarchical representation with a single algorithm.
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Trainable Feature Transform
Inspired by Nature?
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L'Avion III de Clement Ader, 1897
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Do not go TOO FAR
4 Types of Deep Architecture
Feed-Forward : multilayer neural nets, convolutional nets
Feed-Back : Stacked Sparse Coding, Deconvolutional Nets
Bi-Directional : Deep Boltzman Machine, Stacked Auto-Encoders
Recurrent : Recurrent Nets, Long-Short Term Memory
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Really need DEEP?
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What is Approach?
multi layers...
but...each layer has
complex model
with small data
The researchers say even they weren’t sure this new approach (152 layers!) was going to be successful – until it was.
“We even didn’t believe this single idea could be so significant,”
said Jian Sun, a principal research manager at Microsoft Research
is DL Omnipotent?
Applications
Scene Recognition (CNN)
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Visual Style Recognition (CNN)
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Object Detection (R-CNN)
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Image Captioning (CNN+LSTM)
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Segmentation (DeconvNet)
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Deep Visuomotor Control
(CNN)
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Neural Style (CNN)
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Deep Learning Trends
- Multi Rresource(Image+Voice) -> 1 Label
- DL Best Practice -> Theory
How to study deep learning?
관심있는 연구주제에 대한 Data를 얻을 수 있는지 먼저 볼것!!!
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Deep Learning Tools
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Refer to
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TensorFlow
Refer to "https://www.udacity.com/course/deep-learning--ud730"
TensorFlow : N-Dimension Arrays
- Scala : 0-D Tensor
- Vector : 1-D Tensor
- Matrix : 2-D Tensor
- Tensor : over 3-D
How many Dimension need?
for Basic Image Processing
X = [B, W, H, C]
SGD
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정해진 입력데이터 Random하게 무한히 반복하여 트래이닝해도 결과에 영향을 주지 않는다.
Basic Logistic Regression
a example of Machine Learning
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Basic Logistic Regression
a example of Machine Learning
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Basic Logistic Regression
a example of Machine Learning
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AlexNet
a example of Deep Learning
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GoogLeNet
a example of Deep Learning
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A simple ReLU Network를 As Matrix Operation으로 처리 가능하다.
Generalization Performance
ML : 파라미터가 많아지면 성능이 나빠진다.
DL : 파라미터가 많아지면 성능이 좋아진다.
Deep Learning & TensorFlow
By SURK PARK
Deep Learning & TensorFlow
- 1,084