hopesdad Goto Github PK

followers: 57.0 following: 55.0 repos: 76.0 gists: 1.0

Name: youngsik won

Type: User

Company: KB Life

Bio: I'm a Dachshund owner and a developer who wants to improve metacognition

Location: South Korea

Blog: [email protected]

💫 About Me:

I am a developer with a goal to harness the power of metacognition to convey complex development concepts in a simple and clear manner, making them accessible to everyone. I enjoy dedicating my early morning hours to fitness, using it as a means to maintain energy and focus in my daily life. Moreover, spending time with my beloved pet, Hopu, provides me with endless inspiration and relaxation. I aim to apply the insights gained from my daily life with Hopu to solve problems in a more creative and efficient manner in my development work

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youngsik won's Projects

goquality-dev-contents

{ 고퀄리티 :zap: 개발 컨텐츠 모음 }

gs-relational-data-access

Accessing Relational Data using JDBC with Spring :: Learn how to access relational data with Spring.

gwonsungjun

gym

A toolkit for developing and comparing reinforcement learning algorithms.

hopesdad

- Youngsik

i-want-to-study-data-science

데이터 사이언스를 공부하고 싶은 분들을 위한 글

java-was

웹 애플리케이션 서버를 구현하는 실습

junior-recruit-scheduler

주니어 개발자 채용 정보

justin-do-it-react

두잇! 리액트 프로그래밍 정석을 선택해 준 독자 여러분 반갑습니다. 이곳은 책에서 사용된 예제코드를 모아둔 깃헙 페이지 입니다.

kaggle-knowhow

Kaggle-Knowhow(Korean Ver) 한국분들을 위한 Kaggle 자료 모음입니다

kb-albert-ko

KB 국민은행에서 제공하는 경제/금융 도메인에 특화된 PLM(ALBERT) 모델

keras

Deep Learning for humans

kogpt2

Korean GPT-2 pretrained cased (KoGPT2)

machine-learning

머신러닝 입문자 혹은 스터디를 준비하시는 분들에게 도움이 되고자 만든 repository입니다. (This repository is intented for helping whom are interested in machine learning study)

mathematics

metaflow

Build and manage real-life data science projects with ease.

multithreaded-estimators

Multithreading inference in Tensorflow Estimators

mysql_basic_practice

SQL 학습 내용을 정리합니다.

neuraxle

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.