Data Science Resources
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개요
- 제 개인 참조하려고 만든 게시글입니다.
- 언제나 좋은 글 및 싸이트, 패키지를 만들어 배포하는 모든 Data Scientist, Analyst 분들 존경합니다.
(1) Tools
I. 머신러닝/딥러닝 관련 자료
(1) 머신러닝
- XGBoost
- Lightgbm
- Documentation: https://lightgbm.readthedocs.io/en/latest/
- LightGBM R-Packages
- Regression metrics review I
- Weighted Median
- Evaluation Metrics for Classification Problems: Quick Examples + References
- Decision Trees: “Gini” vs. “Entropy” criteria
- Understanding ROC curves
- Learning to Rank using Gradient Descent
- Overview of further developments of RankNet
- RankLib
- Learning to Rank Overview
- Clustering
- Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python
- Matrix Factorization
- t-SNE
- Significance of the Difference between the Areas under Two Independent ROC Curves
- Tidy Modeling with R
(2) 딥러닝 논문
- 자연어처리
- Computer Vision
(3) 추천시스템
(4) 강의자료
(5) 딥러닝 논문 로드맵
- 고려대학교 산업공대
II. 통계
(1) 구조방정식
- SmartPLS: 기본 튜토리얼 및 원서 제공
III. 문서 자동화 & autoEDA
(1) R 기반
- Rendering PowerPoint Presentations with RStudio
- RMarkdown Driven Development (RmdDD)
- The Landscape of R Packages for Automated Exploratory Data Analysis
(2) Python 기반
IV. 데이터과학을 위한 Tools
V. GUI (Graphical User Interface)
(1) Python
- Tkinter
- [Sample Tutorial](Cats vs Dogs Classification (with 98.7% Accuracy) using CNN Keras – Deep Learning Project for Beginner)
VI. 시각화
(1) 공간시각화
(2) 파이썬 시각화
(3) 파이썬 대시보드
- Stephen Kilcommins. (2021). Streamlit vs Dash vs Voilà vs Panel — Battle of The Python Dashboarding Giants
V. MLOps
- Rafi Kurlansik and ML SMEs at Databricks. (2021). Need for Data-centric ML Platforms:Why switching from a model-centric to a data-centric approach solves the biggest challenges facing MLOps
