TF code examples for Deep learning

주로 tf.layers를 이용했으며, 추후 tf.contrib.slim으로 refactoring한 파일도 추가예정

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01. Tutorial of implementing DL model by OOP

• OOP style로 Deep learning 모형을 짜는 예제 (with mnist)
• Tutorial of implementing DL model by OOP.ipynb

02. Tutorial of implementing Drop out

• Drop out을 적용하는 예제 (with mnist)
• Tutorial of implementing Drop out.ipynb

03. Tutorial of implementing Batch normalization

• Batch normalization을 적용하는 예제 (with mnist)
• Tutorial of implementing Batch normalization.ipynb

04. Tutorial of implementing Batch norm and Drop out

• Batch normalization과 Drop out을 동시 적용하는 예제 (with fashion mnist)
• Tutorial of implementing Batch normalization and Drop out.ipynb

05. Tutorial of implementing Transfer learning

• Transfer learning에 대한 예제 (with mnist)
• Tutorial of implementing Transfer learning.ipynb

06. Tutorial of implementing Transfer learning with model Zoo

• Vggnet, Googlenet, Resnet

07. Tutorial of implementing Class activation map

• "Learning Deep Features for Discriminative Localization" 논문에서 제안된 Class activation map의 간단한 구현 (with mnist clutter)
• Tutorial of implementing Class activation map.ipynb

08. Tutorial of implementing Word2Vec

• Word2Vec의 skip-gram의 간단한 구현
• 추후 Word2Vec cbow 구현 추가
• Tutorial of implementing Word2Vec.ipynb

09. Tutorial of implementing Sequence classification with RNN series

• Character level GRU로 가변길이의 영어단어의 긍/부정을 예측하는 모형을 학습
• Tutorial of implementing Sequence classification with RNN series.ipynb

10. Tutorial of implementing Variational Auto-Encoder

• "Auto-Encoding Variational Bayes" 논문에서 소개된 Variational Auto-Encoder의 간단한 구현 (with mnist)
• Tutorial of implementing Variational Auto-Encoder.ipynb

11. Tutorial of implementing Generative Adversarial Net

• "Generative Adversarial Nets" 논문에서 소개된 Generative Adversarial Net의 간단한 구현 (with mnist)
• Tutorial of implementing Generative Adversarial Net.ipynb

12. Tutorial of implementing Conditional GAN

• "Conditional Generative Adversarial Nets" 논문에서 소개된 Conditional Generative Adversarial Net의 간단한 구현 (with mnist)
• Tutorial of implementing Conditional GAN.ipynb

13. Sentence classification by MorphConv

• "Convolution Neural Networks for Sentence Classification" 논문에서 소개된 모형의 간단한 구현 (with naver movie review)
• Sentence classification by MorphConv.ipynb