SHEPHEXD
CAN MACHINES THINK LIKE HUMANS?
스토리지란?
인프라에서 저장소의 역할을 담당하는 스토리지의 개념에 대해서 정리하였습니다. 인프라 관련 도서1 및 위키피디아를 참고하여 작성하였습니다.
-
정송화, 김엉선, 전성민, 2018, 개발자도 궁금한 IT 인프라, http://www.kyobobook.co.kr/product/detailViewKor.laf?mallGb=KOR&ejkGb=KOR&barcode=9791188621224 ↩
CI/CD 란?
DevOps의 Delivery Pipline를 구축하기 위해 필요한 CI/CD의 개념에 대해 소개하려고 합니다.
Journey of LinchFin(2) - AssetNetwork
What is the best way to represent the relations of assets? I suggest a way to represent the asset’s relation called, asset network, based on the graph theory.
Journey of LinchFin(1) - Introduction
LinchFin is a library for asset investors. In this article, I want to introduce about the concept and flow of LinchFin.
Machine Learning Asset Allocation with python - Hierarchcial Risk Parity
머신러닝을 이용한 자산 배분 알고리즘의 하나로 계층형 리스크 패리티에 대해서 소개하고자 합니다. 해당 내용은 Advances in Financial Machine Learning1 내용을 참고하였습니다.
-
Advances in Financial Machine Learning, Marcos Lopez de Prado (https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089) ↩
Design Pattern (1) - Introduction
객체 지향 설계에 사용되는 다양한 패턴들을 정리하려고 작성하였습니다. 컨텐츠 내용은 해당 책1을 참조하였습니다.
-
GOF의 디자인 패턴, 프로텍미디어 ↩
Probabilistic Graphical Model(2) - Preliminaries
This post is series of the instruction notes for the Coursera lecture[^1]
In this post, I will introduce two preliminaries before studying PGM.
- Joint Distribution
- Factor
Probabilistic Graphical Model(1) - Introduction
This post is series of the instruction notes for the Coursera lecture1
The Proabilistic Graphical Model let us understand the world with relations and factors.
Also, Most of the machine learning model can be represened as an probabilistic graph.
There are three main subjects for Probabilistic Graph Models
- Representation
- Learning
- Reasoning
-
https://www.coursera.org/learn/probabilistic-graphical-models ↩