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  • Type MOOC course
  • Period Always open
  • hr Study freely
  • Course approval method Automatic approval

Instructor Introduction

Lecture plan

강의목록
  1. A. Introduction
    1. Introduction
  2. B. Basic Machine Learning: Supervised Learning
    1. Overview
    1. Hypothesis Set
    1. Loss Function - Preview
    1. Probability in 5 minutes
    1. Loss Function
    1. Optimization methods
    1. Backpropagation
    1. Gradient-Based Optimization
    1. Summary
    1. Questions
  3. C. Text Classification & Sentence Representation
    1. Overview
    1. How to represent sentence & token?
    1. CBoW & RN & CNN
    1. Self Attention & RNN
    1. Summary
    1. Questions
  4. D. Neural Language Models
    1. Overview: Language Modelling
    1. Autoregressive language modelling
    1. N-Gram Language Models
    1. Neural N-Gram Language Model
    1. Long Term Dependency
    1. Summary
    1. Questions
  5. E. Neural Machine Translation
    1. Overview: a bit of history remark
    1. Encoder & Decoder
    1. RNN Neural Machine Translation
    1. Questions
  6. F. Case Study
    1. Learning to Describe Multimedia
    1. Fully Character-Level Machine Translation
    1. Meta-Learning of Low-Resource Neural Machine Translation
    1. Real-Time Translation Learning to Decode
    1. Questions
  7. G. Finishing the lecture
    1. Finishing the lecture

Additional Info

[Publisher] 커넥트재단 MOOC 컨텐츠: 장지수