{"id":22066425,"url":"https://github.com/daun-io/study-data-science","last_synced_at":"2025-05-13T01:55:28.596Z","repository":{"id":133440186,"uuid":"79625359","full_name":"daun-io/Study-Data-Science","owner":"daun-io","description":"Practical data science notebooks that I used to study at 2016","archived":false,"fork":false,"pushed_at":"2017-01-25T03:29:59.000Z","size":7563,"stargazers_count":48,"open_issues_count":0,"forks_count":7,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-05-13T01:55:22.350Z","etag":null,"topics":["data-science","jupyter-notebook","machine-learning","tensorflow"],"latest_commit_sha":null,"homepage":"https://nyanye.com","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/daun-io.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2017-01-21T05:15:01.000Z","updated_at":"2024-01-04T16:10:44.000Z","dependencies_parsed_at":null,"dependency_job_id":"e4426d57-f42d-4a16-b364-03c257cf1aac","html_url":"https://github.com/daun-io/Study-Data-Science","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daun-io%2FStudy-Data-Science","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daun-io%2FStudy-Data-Science/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daun-io%2FStudy-Data-Science/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daun-io%2FStudy-Data-Science/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/daun-io","download_url":"https://codeload.github.com/daun-io/Study-Data-Science/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253856639,"owners_count":21974577,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","jupyter-notebook","machine-learning","tensorflow"],"created_at":"2024-11-30T19:28:04.103Z","updated_at":"2025-05-13T01:55:28.574Z","avatar_url":"https://github.com/daun-io.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Study Data Science\n\n## 데이터 과학 실습\n\nData science from scratch, Coursera, udacity, Awesome list, [모두를 위한 머신러닝](https://hunkim.github.io/ml/) 등의 자료를 보며 수식, 설명, 코드를 곁들여 Jupyter notebook으로 작성한 데이터 과학 실습 저장소입니다.\n\nGithub에서 Jupyter notebook viewer를 지원하기 때문에 웹에서 바로 내용을 볼 수 있습니다.\n\n이 저장소의 실습내용은 [여기](https://nyanye.com/articles/)서도 확인하실 수 있습니다. 이미지나 수식이 깨진 경우 저기서 확인해주시면 됩니다 :)\n\n자료에 오/탈자, 누락된 부분이 있을 수 있습니다. 잘못된 내용이 있다면 Pull Request나 issue를 올려주시면 감사하겠습니다 :)\n\n## 개발환경\n\nOS : Windows 10 64bit Python 3.5+  \nTensorflow CPU-only  \nJupyter notebook\n\n## 목록\n\n[#. Public Datasets](/00-Datasets)  \n[1. 밑바닥부터 시작하는 데이터 과학(Data Science from Scratch)](/01-Data-Science-From-Scratch)  \n[1.0. 원본소스코드(Original Source Code)](/01-Data-Science-From-Scratch/00-원본소스코드(source_code))  \n[1.1. 들어가기(Intro)](/01-Data-Science-From-Scratch/01-들어가기(Introduction))  \n[1.2. 시각화(Visualization)](/01-Data-Science-From-Scratch/02-시각화(Visualization))  \n[1.3. 선형대수(Linear Algebra)](/01-Data-Science-From-Scratch/03-선형대수(Linear_Algebra))  \n[1.4. 확률과 통계(Probability \u0026 Statistics)](/01-Data-Science-From-Scratch/04-확률\u0026통계(Probability\u0026Statistics))  \n[1.5. 최적화 알고리즘(Optimization Algorithm)](/01-Data-Science-From-Scratch/05-최적화_알고리즘(Optimization_Algorithm))  \n[1.6. 데이터 탐색(Data Exploration)](/01-Data-Science-From-Scratch/06-데이터_탐색(Data_exploration))  \n[1.7. 데이터 수집과 처리(Collecting and Processing Data)](/01-Data-Science-From-Scratch/07-데이터_수집\u0026처리(Collecting\u0026Processing_Data))  \n[1.8. 기계학습(Machine Learning)](/01-Data-Science-From-Scratch/08-기계학습(Machine_Learning))\n\n[2. Tensorflow 기계학습 실습(Machine Learning with Tensorflow)](/02-Tensorflow)  \n[2.0. Hello World 부터 선형회귀까지(Linear Regression from Hello World)](/02-Tensorflow/00-Hello_Tensor\u0026Linear_Regression)  \n[2.1. 비용(Cost)](/02-Tensorflow/01-Cost)  \n[2.2. 다중 선형 회귀](/02-Tensorflow/02-Multivariable_Linear_Regression)  \n[2.3. 로지스틱 회귀(Logistic Regression)](/02-Tensorflow/03-Logistic_Classification)  \n[2.4. 다항 분류(Multinomial Classification)](/02-Tensorflow/04-Multinomial_Classification)  \n[2.5. 기계학습 팁(Tips for Machine Learning)](/02-Tensorflow/05-Tip\u0026Tricks)  \n[2.6. 심층학습(Deep Learning)](/02-Tensorflow/06-Deep_Learning)\n\n## Update\n\nMNIST Challenge, CNN, RNN도 [2.6. 심층학습(Deep Learning)](/02-Tensorflow/06-Deep_Learning)에 포함.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaun-io%2Fstudy-data-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdaun-io%2Fstudy-data-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaun-io%2Fstudy-data-science/lists"}