{"id":23364054,"url":"https://github.com/rickiepark/deep-learning-with-python-2nd","last_synced_at":"2025-04-10T12:51:25.517Z","repository":{"id":43104024,"uuid":"480320421","full_name":"rickiepark/deep-learning-with-python-2nd","owner":"rickiepark","description":"\u003c케라스 창시자에게 배우는 딥러닝 2판\u003e 도서의 코드 저장소","archived":false,"fork":false,"pushed_at":"2024-03-09T09:51:19.000Z","size":50762,"stargazers_count":72,"open_issues_count":1,"forks_count":91,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-24T11:38:24.271Z","etag":null,"topics":["cnn","deep-learning","gan","image-augmentation","image-classification","image-segmentation","image-style-transfer","keras","keras-tuner","machine-translation","mixed-precision","multi-gpu","neural-network","rnn","tensorflow","text-classification","text-generation","time-series","tpu","transformer"],"latest_commit_sha":null,"homepage":"https://bit.ly/kerasdl2","language":"Jupyter 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Notebook","readme":"# 케라스 창시자에게 배우는 딥러닝 2판\n\n![cover](cover.jpg)\n\n이 저장소는 \u003c[케라스 창시자에게 배우는 딥러닝 2판](https://tensorflow.blog/kerasdl2/)\u003e 책의 주피터 노트북을 담고 있습니다. 이 책은 케라스(Keras) 라이브러리의 창시자이고 현재 구글의 소프트웨어 엔지니어인 프랑소와 숄레(François Chollet)가 쓴 \u003cDeep Learning with Python 2nd Edition\u003e의 번역서입니다. 번역서는 628페이지, 풀 컬러, 양장으로 제작되었습니다.\n\n* 교보문고 https://bit.ly/3dv256v\n* 예스24 https://bit.ly/3QP05oe\n* 알라딘 https://bit.ly/3QrGCKt\n\n각 목차에 해당하는 노트북은 다음을 참고하세요.\n\n* 2장 [신경망의 수학적 구성 요소](chapter02_mathematical-building-blocks.ipynb)\n* 3장 [케라스와 텐서플로 소개](chapter03_introduction-to-keras-and-tf.ipynb)\n* 4장 [신경망 시작하기: 분류와 회귀](chapter04_getting-started-with-neural-networks.ipynb)\n* 5장 [머신 러닝의 기본 요소](chapter05_fundamentals-of-ml.ipynb)\n* 7장 [케라스 완전 정복](chapter07_working-with-keras.ipynb)\n* 8장 [컴퓨터 비전을 위한 딥러닝](chapter08_intro-to-dl-for-computer-vision.ipynb)\n* 9장 컴퓨터 비전을 위한 고급 딥러닝\n  * 9.2 [이미지 분할 예제](chapter09_part01_image-segmentation.ipynb)\n  * 9.3 [최신 컨브넷 아키텍처 패턴](chapter09_part02_modern-convnet-architecture-patterns.ipynb)\n  * 9.4 [컨브넷이 학습한 것 해석하기](chapter09_part03_interpreting-what-convnets-learn.ipynb)\n* 10장 [시계열을 위한 딥러닝](chapter10_dl-for-timeseries.ipynb)\n* 11장 텍스트를 위한 딥러닝\n  * 11.2, 11.3 [텍스트 데이터 준비, 단어 그룹을 표현하는 두 가지 방법: 집합과 시퀀스](chapter11_part01_introduction.ipynb)\n  * 11.3.3 [단어를 시퀀스로 처리하기: 시퀀스 모델 방식](chapter11_part02_sequence-models.ipynb)\n  * 11.4 [트랜스포머 아키텍처](chapter11_part03_transformer.ipynb)\n  * 11.5 [텍스트 분류를 넘어: 시퀀스-투-시퀀스 학습](chapter11_part04_sequence-to-sequence-learning.ipynb)\n* 12장 생성 모델을 위한 딥러닝\n  * 12.1 [텍스트 생성](chapter12_part01_text-generation.ipynb)\n  * 12.2 [딥드림](chapter12_part02_deep-dream.ipynb)\n  * 12.3 [뉴럴 스타일 트랜스퍼](chapter12_part03_neural-style-transfer.ipynb)\n  * 12.4 [변이형 오토인코더를 사용한 이미지 생성](chapter12_part04_variational-autoencoders.ipynb)\n  * 12.5 [생성적 적대 신경망 소개](chapter12_part05_gans.ipynb)\n* 13장 [실전 문제 해결을 위한 모범 사례](chapter13_best-practices-for-the-real-world.ipynb)\n* 14장 [결론](chapter14_conclusions.ipynb)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frickiepark%2Fdeep-learning-with-python-2nd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frickiepark%2Fdeep-learning-with-python-2nd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frickiepark%2Fdeep-learning-with-python-2nd/lists"}