📘딥러닝기본-전체

딥러닝 이론

1. 딥러닝 개요

2. 딥러닝 학습

3. 활성화 함수(Activaion Function)

4. 손실 함수(Loss Function)

5. 역전파(Back Propagation)

6. 최적화와 경사하강법(Optimization)

7. 성능평가지표(Evaluation)

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딥러닝 모델

8. CNN(Convolutional Neural Net.)-1

9. CNN(Convolutional Neural Net.)-2

10. RNN(Recurrent Neural Net.)

11. LSTM (Long Short-Term Memory)

12. CNN based Models

• 12-1. AlexNet

• 12-2. VGGNet

• 12-3. Inception (GoogLeNet)

• 12-4. ResNet

• 12-5. Xception

• 12-6. MobileNet

13. Object Detection

• 13-0. NMS (Non Maximum Suppression)

• 13-1. R-CNN

• 13-2. Overfeat

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딥러닝 기법

Gradients Vanishing & Exploding

Weight Initialization

Weight Decay (L2 Regularization)

Input Data Scaling

Batch Normalization

Transfer Learning, Fine Tuning