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Deep Learning

Presentation list

• 04/02/20 :

  • (Chapter 6)Deep Feedforward Networks (by Jee)
    link, download

  • Information Theory(~Cross Entropy) (by Suk)
    link, download

  • Implementation of Backpropagation(with codes) (by Kang)
    link, download

• 04/09/20 :

  • (Chapter 7.1-7)Regularization for Deep Learning(by Suk)
    link, download

  • Code Implementation (by Kang)
    link, download, code

• 04/16/20 :
  • (Chapter 7.1-7)Regularization for Deep Learning(2)(by Suk)
    link download
• 04/23/20 :

  • (Chapter 7.8-14)Regularization for Deep Learning(by Jee)
    link, download

  • (Chapter 8.1-2)Optimization for Training Deep Models(by Kang)
    link, download

• 05/14/20 :

  • (Chapter 8.3-5)Optimization for Training Deep Models(by Kang)
    link, download

  • (Chapter 8.6-7)Optimization for Training Deep Models(by Suk)
    link, download

• 05/21/20 :

  • Linear Regression Implementation(by Jung)
    report, code

  • (Chapter 9)Convolutional Networks(by Jee)
    code, link, download

• 05/28/20 :

  • (Chapter 10.1-5)Sequence Modeling : Recurrent and Recursive Nets(by Hong)
    link, download

  • (Chapter 10.6-11)Sequence Modeling : Recurrent and Recursive Nets(by Suk)
    link, download

• 06/04/20 :

  • Attention Model (by Suk)
    code

  • (Chapter 13)Linear Factor Models(by Jung)
    link, download

• 06/18/20 :
  • (Chapter 15)Representation Learning(by Kang)
    Slide, Download
• 07/09/20 :
  • (Chapter 16)Structured Probabilistic Models for Deep Learning(by Hong)
    Slide, Download
• 07/23/20 :
  • (Chapter 18)Confronting the Partition Function(by Cho)
    [Slide], [Download]
• 07/30/20 :
  • (Chapter 19)Approximate Inference(by Suk)
    Slide, Download
• 08/06/20 :
  • (Chapter 20)Generavtive adversarial model(by Hong)
    Slide, Download