# Index

* [소개글](master)
* [서문](preface)
* [Index](index_list)
* [딥러닝을 활용한 자연어 처리 개요](00-cover)
  * [자연어 처리란 무엇일까?](00-cover/01-intro)
  * [딥러닝 소개](00-cover/02-deeplearning)
  * [왜 자연어 처리는 어려울까?](00-cover/03-why-nlp-difficult)
  * [무엇이 한국어 자연어 처리를 더욱 어렵게 만들까?](00-cover/04-korean-is-hell)
  * [자연어 처리의 최근 추세](00-cover/05-trends)
* [기초 수학](00-cover-1)
  * [서문](00-cover-1/01-intro)
  * [랜덤 변수와 확률 분포](00-cover-1/02-prob-dist)
  * [쉬어가기: 몬티 홀 문제](00-cover-1/03-monty-hall)
  * [기대값과 샘플링](00-cover-1/04-sampling)
  * [Maximum Likelihood Estimation](00-cover-1/05-mle)
  * [정보 이론](00-cover-1/06-information)
  * [쉬어가기: MSE 손실 함수와 확률 분포 함수](00-cover-1/07-mse)
  * [마치며](00-cover-1/08-conclusion)
* [Hello 파이토치](00-cover-2)
  * [딥러닝을 시작하기 전에](00-cover-2/01-intro)
  * [설치 방법](00-cover-2/02-how-to-install)
  * [짧은 튜토리얼](00-cover-2/03-hello-pytorch)
  * [쉬어가기: 윈도우즈 개발 환경 구축](00-cover-2/04-how_to_install_wins)
* [전처리](00-cover-3)
  * [전처리란](00-cover-3/01-intro)
  * [코퍼스 수집](00-cover-3/02-collecting-corpus)
  * [코퍼스 정제](00-cover-3/03-cleaning-corpus)
  * [분절](00-cover-3/04-tokenization)
  * [병렬 코퍼스 정렬](00-cover-3/05-align)
  * [서브워드 분절](00-cover-3/06-bpe)
  * [분절 복원](00-cover-3/07-detokenization)
  * [토치텍스트](00-cover-3/08-torchtext)
* [유사성과 모호성](00-cover-4)
  * [단어의 의미](00-cover-4/01-intro)
  * [One-hot 인코딩](00-cover-4/02-one-hot-encoding)
  * [시소러스를 활용한 단어 의미 파악](00-cover-4/03-wordnet)
  * [특징](00-cover-4/04-feature)
  * [특징 추출하기: TF-IDF](00-cover-4/05-tf-idf)
  * [특징 벡터 만들기](00-cover-4/06-vectorization)
  * [특징 유사도 구하기](00-cover-4/07-similarity)
  * [단어 중의성 해소](00-cover-4/08-wsd)
  * [Selectional Preference](00-cover-4/09-selectional-preference)
  * [마치며](00-cover-4/10-conclusion)
* [단어 임베딩](00-cover-5)
  * [들어가며](00-cover-5/01-intro)
  * [차원 축소](00-cover-5/02-dimension-reduction)
  * [흔한 오해 1](00-cover-5/03-myth)
  * [Word2Vec](00-cover-5/04-word2vec)
  * [GloVe](00-cover-5/05-glove)
  * [Word2Vec 예제](00-cover-5/06-example)
  * [마치며](00-cover-5/07-conclusion)
* [시퀀스 모델링](00-cover-6)
  * [들어가며](00-cover-6/01-intro)
  * [Recurrent Neural Network](00-cover-6/02-rnn)
  * [Long Short Term Memory](00-cover-6/03-lstm)
  * [Gated Recurrent Unit](00-cover-6/04-gru)
  * [그래디언트 클리핑](00-cover-6/05-gradient-clipping)
  * [마치며](00-cover-6/06-conclusion)
* [텍스트 분류](00-cover-7)
  * [들어가기](00-cover-7/01-intro)
  * [나이브 베이즈를 활용하기](00-cover-7/02-naive-bayes)
  * [흔한 오해 2](00-cover-7/03-myth)
  * [RNN을 활용하기](00-cover-7/04-rnn)
  * [CNN을 활용하기](00-cover-7/05-cnn)
  * [쉬어가기: 멀티 레이블 분류](00-cover-7/06-multi_classification)
  * [마치며](00-cover-7/07-conclusion)
* [언어 모델링](00-cover-8)
  * [들어가며](00-cover-8/01-intro)
  * [n-gram](00-cover-8/02-n-gram)
  * [언어 모델의 평가 방법](00-cover-8/03-perpexity)
  * [SRILM을 활용한 n-gram 실습](00-cover-8/04-srilm)
  * [NNLM](00-cover-8/05-nnlm)
  * [언어 모델의 활용](00-cover-8/06-application)
  * [마치며](00-cover-8/07-conclusion)
* [신경망 기계번역](00-cover-9)
  * [들어가며](00-cover-9/01-intro)
  * [Sequence-to-Sequence](00-cover-9/02-seq2seq)
  * [Attention](00-cover-9/03-attention)
  * [Input Feeding](00-cover-9/04-input-feeding)
  * [자기회귀 속성과 Teacher Forcing 훈련 방법](00-cover-9/05-teacher-forcing)
  * [탐색(추론)](00-cover-9/06-beam-search)
  * [성능 평가](00-cover-9/07-eval)
  * [마치며](00-cover-9/08-conclusion)
* [신경망 기계번역 심화 주제](00-cover-10)
  * [다국어 신경망 번역](00-cover-10/01-multilingual-nmt)
  * [단일 언어 코퍼스를 활용하기](00-cover-10/02-monolingual-corpus)
  * [트랜스포머](00-cover-10/03-transformer)
  * [마치며](00-cover-10/04-conclusion)
* [강화학습을 활용한 자연어 생성](00-cover-11)
  * [들어가며](00-cover-11/01-intro)
  * [강화학습 기초](00-cover-11/02-rl_basics)
  * [정책 기반 강화학습](00-cover-11/03-policy-gradient)
  * [자연어 생성에 강화학습 적용하기](00-cover-11/04-characteristic)
  * [강화학습을 활용한 지도학습](00-cover-11/05-supervised-nmt)
  * [강화학습을 활용한 비지도학습](00-cover-11/06-unsupervised-nmt)
  * [마치며](00-cover-11/07-conclusion)
* [듀얼리티 활용](00-cover-12)
  * [들어가며](00-cover-12/01-intro)
  * [듀얼리티를 활용한 지도학습](00-cover-12/02-dsl)
  * [듀얼리티를 활용한 비지도학습](00-cover-12/03-dul)
  * [쉬어가기: Back-translation을 재해석하기](00-cover-12/04-back_translation)
  * [마치며](00-cover-12/05-conclusion)
* [NMT 시스템 구축](00-cover-13)
  * [파이프라인](00-cover-13/01-pipeline)
  * [구글의 NMT](00-cover-13/02-gnmt)
  * [에딘버러 대학의 NMT](00-cover-13/03-nematus)
  * [MS의 NMT](00-cover-13/04-microsoft)
* [전이학습](00-cover-14)
  * [전이학습이란?](00-cover-14/01-intro)
  * [기존의 사전 훈련 방식](00-cover-14/02-previous_work)
  * [ELMo](00-cover-14/03-elmo)
  * [BERT](00-cover-14/04-bert)
  * [GPT-2](00-cover-14/05-gpt)
  * [XLNet](00-cover-14/06-xlnet)
  * [마치며](00-cover-14/07-conclusion)
* [이 책을 마치며](epilogue)
* [참고문헌](references)
