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[cs231n] cs231n - START 본문
Standford 강의 중 CS231n에 대하여 공부하고 정리한다.
강의의 보충 자료인 lecture notes를 토대로 공부하여 이를 정리한다.
Lecture Notes)
1) Image Classification
2) Linear Classification
3) Backpropagation
4) Neural Networks1: Architecture
5) Neural Networks2: Data and Loss
6) Neural Networks3: Learning and Evaluation
7) Convolutional Neural Networks: Architectures, Convolution/Pooling
8) Understanding and Visualizing Convolutional Neural Networks
'Stanford Lectures : AI > CS231n' 카테고리의 다른 글
[cs231n] Note 2: Linear Classification (Loss function) (0) | 2020.12.09 |
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[cs231n] Note 2: Linear Classification (Introduction) (0) | 2020.11.27 |
[cs231n] Note 1: Image Classification (Validation sets for Hyperparameter tuning) (0) | 2020.11.19 |
[cs231n] Note 1: Image Classification (Nearest Neighbor Classifier) (0) | 2020.11.16 |
[cs231n] Note 1: Image Classification (Introduction) (0) | 2020.11.11 |
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