2. 2
인공 신경망
입력층 히든층 출력층
hidden
딥러닝은 인공신경망을 사용하는 기계학습의 한 분야
멀티레이어퍼셉트론이라 불리움
3. 3
심층 신경망; deep neural networks
2개 층 이상
딥러닝은 심층 신경망을 사용하는 기계학습의 한 분야
히든 층이 2개 이상인 인공 신경망, 다층퍼셉트론
4. 4
신경망의 역사
• Progression (1943-1960)
• First Mathematical model of neurons, Pitts & McCulloch (1943)
• Beginning of artificial neural networks–Perceptron, Rosenblatt (1958)
• Degression (1960-1980)
• Perceptron can’t even learn the XOR function
• We don’t know how to train MLP
• 1963 Backpropagation (Bryson et al.)
• Progression (1980-)
• 1986 Backpropagation reinvented
• Degression (1993-)
• SVM: Support Vector Machine is developed by Vapnik et al.[1995]
• Graphical models are becoming more and more popular
• Training deeper networks consistently yields poor results.
• However, Yann LeCun (1998) developed deep convolutional neural networks
• Progression (2006-)
• Deep Belief Networks (DBN) by Hinton et al. (2006)
• Deep Autoencoder based networks by Greedy Layer-Wise Training of Deep Networks. Bengio et al.
• Convolutional neural networks running on GPUs
• AlexNet (2012). Krizhevsky et al.
source: http://www.cs.cmu.edu/~10701/slides/Perceptron_Reading_Material.pdf
7. Neural Networks
Multi-Layer Perceptron
DBN
CNN
RNN
RBM AE
2-Layer Perceptron ~ Regression
Linear
Logistic
Softmax
Deep
Neural
Networks
GAN
Reinforcement Learning
Supervised Learning
Unsupervised Learning
7
심층학습; Deep Learning
Discriminative Model
Generative Model
심층신경망을 사용하는 기계학습 분야
신경망 자체
심층신경망을 이용한 기존/신규 기계학습 방법
8. 8
Machine Learning Data Mining
Decision Support System
Big Data
Cloud ~ Web
Artificial Intelligence
Image Processing
Computer Vision
Machine Vision
Neural Networks
Pattern Recognition
관련 연구분야
Data Science
