강의목록 Help

A. Introduction
- Introduction
  
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  2019.07.24
  
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B. Basic Machine Learning: Supervised Learning
- Overview
  
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  2019.07.14
  
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- Hypothesis Set
  
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  2019.07.24
  
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- Loss Function - Preview
  
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  2019.05.01
  
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- Probability in 5 minutes
  
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  2018.07.27
  
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- Loss Function
  
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  2019.06.01
  
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- Optimization methods
  
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  2019.05.02
  
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- Backpropagation
  
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  2019.07.14
  
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- Gradient-Based Optimization
  
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  2019.05.15
  
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- Summary
  
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  2019.05.02
  
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- Questions
  
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  2019.06.01
  
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C. Text Classification & Sentence Representation
- Overview
  
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  2019.05.09
  
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- How to represent sentence & token?
  
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  2019.05.03
  
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- CBoW & RN & CNN
  
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  2019.05.15
  
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- Self Attention & RNN
  
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  2019.06.02
  
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- Summary
  
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  2019.03.22
  
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- Questions
  
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  2019.05.19
  
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D. Neural Language Models
- Overview: Language Modelling
  
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  2019.02.06
  
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- Autoregressive language modelling
  
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  2019.07.20
  
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- N-Gram Language Models
  
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  2019.05.23
  
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- Neural N-Gram Language Model
  
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  2019.03.27
  
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- Long Term Dependency
  
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  2019.03.04
  
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- Summary
  
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  2018.07.27
  
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- Questions
  
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  2018.07.27
  
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E. Neural Machine Translation
- Overview: a bit of history remark
  
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  2018.08.03
  
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- Encoder & Decoder
  
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  2018.07.27
  
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- RNN Neural Machine Translation
  
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  2018.07.27
  
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- Questions
  
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  2019.04.11
  
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F. Case Study
- Learning to Describe Multimedia
  
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  2019.05.14
  
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- Fully Character-Level Machine Translation
  
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  2018.07.27
  
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- Meta-Learning of Low-Resource Neural Machine Translation
  
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  2019.05.11
  
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- Real-Time Translation Learning to Decode
  
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  2018.07.27
  
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- Questions
  
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  2018.07.27
  
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G. Finishing the lecture
- Finishing the lecture
  
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  2019.03.02
  
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