2023. 1. 20. 15:15ㆍ수집/IT
Do it BERT 와 GPT로 배우는 자연어 처리 정리
http://www.yes24.com/Product/Goods/105294979
Do it! BERT와 GPT로 배우는 자연어 처리 - YES24
‘사람 말을 알아듣는 AI’를 만들어 보자!트랜스포머 핵심 원리부터 문서 분류, 기계 독해, 문장 생성 실습까지!사람 말을 알아듣고 처리하는 인공지능은 어떻게 만들까? 이 책은 딥러닝 기반
www.yes24.com
토큰화 문서/소스
https://github.com/huggingface/tokenizers/tree/main/bindings/python
GitHub - huggingface/tokenizers: 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production - GitHub - huggingface/tokenizers: 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
github.com
바이트 페어 인코딩 기계번영에 처음 도입해본 논문
https://github.com/huggingface/tokenizers/tree/main/bindings/python
GitHub - huggingface/tokenizers: 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production - GitHub - huggingface/tokenizers: 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
github.com
워드 피스 기법을 제안한 논문
https://research.google.com/pubs/archive/37842.pdf
트랜스포머 원 논문
https://arxiv.org/abs/1706.03762
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new
arxiv.org
트랜스포머 모델 구조를 파이토치 코드로 해설한 블로그 페이지
https://nlp.seas.harvard.edu/2018/04/03/attention.html
The Annotated Transformer
———————- There is now a new version of this blog post updated for modern PyTorch. ———————- from IPython.display import Image Image(filename='images/aiayn.png') The Transformer from “Attention is All You Need” has been on a l
nlp.seas.harvard.edu
트랜스포머 계산과정을 시각화 한 글
https://jalammar.github.io/illustrated-transformer/
The Illustrated Transformer
Discussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Japanese, Korean, Persian, Russian, Spanish, Vietnamese Watch: MIT
jalammar.github.io
Bert 논문
https://arxiv.org/abs/1810.04805
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unla
arxiv.org
페이스북 RoBERTa
https://arxiv.org/abs/1907.11692
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperpar
arxiv.org
허깅페이스 트랜스포머
https://huggingface.co/docs/transformers/index
🤗 Transformers
Reinforcement learning models
huggingface.co
강필성 교수 <텍스트 애널리틱스>
https://github.com/pilsung-kang/Text-Analytics
GitHub - pilsung-kang/Text-Analytics: Unstructured Data Analysis (Graduate) @Korea University
Unstructured Data Analysis (Graduate) @Korea University - GitHub - pilsung-kang/Text-Analytics: Unstructured Data Analysis (Graduate) @Korea University
github.com
조경현 교수의 <딥러닝을 이용한 자연어 처리>
https://www.boostcourse.org/ai331
딥러닝을 이용한 자연어 처리
부스트코스 무료 강의
www.boostcourse.org
김현중 님의 <자연어 처리를 위한 머신러닝>
https://github.com/lovit/fastcampus_textml_blogs
GitHub - lovit/fastcampus_textml_blogs: 패스트캠퍼스, 자연어처리를 위한 머신러닝, 수업관련 포스트 입
패스트캠퍼스, 자연어처리를 위한 머신러닝, 수업관련 포스트 입니다. Contribute to lovit/fastcampus_textml_blogs development by creating an account on GitHub.
github.com
서민준 교수의 <자연어 처리를 위한 딥러닝>
https://seominjoon.github.io/kaist-ai605/
KAIST AI605 - Deep Learning for NLP (Spring 2022)
Graduate-level Deep Learning for Natural Language Processing
seominjoon.github.io
크리스토퍼 매닝 교수의 <딥러닝을 활용한 자연어 처리>
https://web.stanford.edu/class/cs224n/
Stanford CS 224N | Natural Language Processing with Deep Learning
Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a tho
web.stanford.edu
<Speech and Language Processing > 3판
https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf
https://web.stanford.edu/~jurafsky/slp3/
Speech and Language Processing
Speech and Language Processing (3rd ed. draft) Dan Jurafsky and James H. Martin Here's our Jan 7, 2023 draft! This draft is mostly a bug-fixing and restructuring release, there are no new chapters. The restructuring moves the applications section earlier,
web.stanford.edu
UC 버클리 자연어 처리 강좌
https://cal-cs288.github.io/sp22/
Home
Just the Class is a modern, highly customizable, responsive Jekyll theme for developing course websites.
cal-cs288.github.io
카네기 멜런 자연어 처리 강좌
https://phontron.com/class/nn4nlp2021/schedule.html
CS 11-747: Neural Networks for NLP
Class Schedule Feb 2, 2021 Feb 4, 2021 Feb 9, 2021 Feb 11, 2021 Feb 16, 2021 Feb 18, 2021 Feb 23, 2021 Feb 25, 2021 Mar 2, 2021 Mar 4, 2021 Mar 9, 2021 Mar 11, 2021 Mar 16, 2021 Mar 18, 2021 Mar 23, 2021 Mar 25, 2021 Mar 30, 2021 Apr 1, 2021 Apr 6, 2021 Ap
phontron.com
GPT1 논문
GPT2 논문
GPT3 논문
https://arxiv.org/abs/2005.14165
Language Models are Few-Shot Learners
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fi
arxiv.org
https://arxiv.org/pdf/2005.14165
'수집 > IT' 카테고리의 다른 글
| 한글 입력 층위의 한글 음절 구성 (3) | 2023.02.16 |
|---|---|
| AWS 아키텍처 기초 참고 사이트 링크 정리 (0) | 2023.01.31 |
| 모델을 이용한 예측 (0) | 2022.12.12 |
| 클린 아키텍처 관련 서적 (0) | 2022.12.10 |
| 정보검색 검색엔진/역색인 (0) | 2022.12.10 |