(These notes are currently in draft form and under development)
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Computer Vision
(CS231N) Lecture11
Understanding and Visualizing Convolutional Neural Networks
(CS231N) Lecture10
합성곱 신경망(Convolutional Neural Networks)
Architectures, Convolution / Pooling Layers
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(CS231N) Lecture9
Putting it together
Minimal Neural Network Case Study
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(CS231N) Lecture8
신경망 학습-3(Neural Networks Part 3)
Learning and Evaluation
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(CS231N) Lecture7
신경망 학습-2(Neural Networks Part 2)
Setting up the Data and the Loss
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(CS231N) Lecture6
신경망 학습-1(Neural Networks Part 1)
Setting up the Architecture
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(CS231N) Lecture5
최적화(Optimization)-2
Backpropagation, Intuitions
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(CS231N) Lecture4
최적화(Optimization)-1
Optimization: Stochastic Gradient Descent
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(CS231N) Lecture3
손실함수(Loss Functions)
Linear classification: Support Vector Machine, Softmax
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(CS231N) Lecture2
이미지 분류(Image Classification)
Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits
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