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머신러닝 시그 세미나_(deep learning for visual recognition)

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Yonghoon Kwon

딥러닝 http://vision0814.tistory.com/

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Observable Data Model Information
Observable Data Model Information Speech Recognition I am a boy
Observable Data Model Information Image Classify Cat
Observable Data Model Information HOW?
• Core visual object recognition Feedback
• Weakness in kernel machine(SVM …): • It does not scale well with sample size. • Based on matching local templates. • the training data is referenced for test data • Local representation VS distributed representation • N N(Neural Network) -> Kernel machine -> Deep NN
• Deep learning is all about deep neural networks • 1949 : Hebbian learning • Donald Hebb : the father of neural networks • 1958 : (single layer) Perceptron • Frank Rosenblatt - Marvin Minsky, 1969 • 1986 : Multilayer Perceptron(Back propagation) • David Rumelhart, Geoffrey Hinton, and Ronald Williams • 2006 : Deep Neural Networks • Geoffrey Hinton and Ruslan Salakhutdinov
Hand- Crafted Features Trainable Generic Classifier
F(X;𝜃) 𝜃 Simple Classifier
Layer 1 Simple Classifier Layer 2 Layer N
Layer 1 Simple Classifier Layer 2 Layer N
Layer 1 Simple Classifier Layer 2 Layer N
Layer 1 Simple Classifier Layer 2 Layer N
Layer 1 Simple Classifier Layer 2 Layer N Trainable Generic Classifier Hand- crafted Features
Layer 1 Simple Classifier Layer 2 Layer N Trainable Generic Classifier Hand- crafted Features
Layer 1 Simple Classifier Layer 2 Layer N
Shallow learning Deep learning feature extraction by domain experts (SIFT, SURF, orb...) automatic feature extraction from data separate modules (feature extractor + trainable classifier) unified model : end-to-end learning (trainable feature + trainable classifier)
i j
• http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html • convolutional neural networks (popular): LeCun • Alex Krizhevsky: Hinton (python, C++) • https://code.google.com/p/cuda-convnet/ • Caffe: UC Berkeley (C++) • http://caffe.berkeleyvision.org/
머신러닝 시그 세미나_(deep learning for visual recognition)
머신러닝 시그 세미나_(deep learning for visual recognition)

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