/tensorflow-2-tutorials

Tensorflow 2.0 tutorials by big data mining lab in Hanyang university ERICA

Primary LanguageJupyter Notebook

Tensorflow 2 Tutorials by BDM Lab

Tensorflow 2 튜토리얼 by BDM Lab (Bigdata Mining Lab in Hanyang Univ. ERICA) - 한국어

Authors: 이재영(Lee, Jaeyoung), 이웅희(Lee, Woonghee), 김종우(Kim, Jongwoo), 양재우(Yang, Jaewoo) 외

(리뉴얼 + 재구성 + 내용추가 작업중) (This repo is in renewal)

Table of Contents

  1. Numpy
    1. Numpy Basic
    2. Numpy Operations 1
    3. Numpy Operations 2
    4. Numpy Broadcasting
    5. Linear Regression
    6. Principal Component Analysis
    7. Scipy Basic (planned)
    8. Logistic regression
  2. Basic Models
    1. Tensorflow Basic
    2. Fully Connected Networks (보완필요)
    3. Convolutional Neural Networks (코드추가완료, 설명미완성)
    4. Recurrent Neural Networks (설명추가필요 및 개선필요)
    5. Custom Models 1 (설명교체필요)
    6. Custom Models 2 (설명교체필요)
    7. Convolutional Neural Networks with Custom Model (코드추가완료, 설명미완성)
    8. Recurrent Neural Networks with Custom Model (planned)
    9. Style Transfer (planned)
    10. Auto Encoder
    11. Regularization for Auto Encoder (설명추가필요)
    12. Attention Mechanism
  3. Generative Models
    1. Restrict Boltzmann Machine
    2. Variational Auto Encoder
    3. Generative Adversarial Networks
    4. Deep Convolution GAN (코드추가완료, 설명미완성)
    5. Conditional GAN
    6. Info GAN (코드추가완료, 설명추가필요)
    7. LS GAN (planned)
    8. WGAN-GP (코드추가완료, 설명미완성)
    9. Progressive GAN
    10. Mixture Density Network
  4. Advanced Techniques
    1. Train on Multi GPUs
  5. Adversarial Attack & Defence
    1. FGSM
    2. DeepFool
  6. Reinforcement Learning
  7. Visualization
    1. Matplotlib 기본
    2. imageio 기초
    3. GradCAM
    4. GradCAM++
    5. Integrated Gradient
  8. Continual Learning
    1. ECW (Overcoming catastrophic forgetting in neural networks)
  9. Recommender Systems
    1. Matrix Factorization with Keras

Contribution Manual

  • 최대한 line-by-line으로 설명하기
  • 수정이 필요한 설명은 언제든 pull request!

Reference