In the great poem by Charles Bukowski,

“No Leaders Please”

Invent yourself, reinvent yourself.

Don’t swim in the same slough.

Invent yourself, reinvent yourself,

and stay out of the clutches of mediocrity.

Change your tone and shape so often that they can never categorize you.

Reinvigorate yourself, and accept what is.

But only on the terms that you have invented and reinvented.

Be self-taught.

And reinvent your life.

Because your must.

It is your life
and its history
and the present
belong only to you.

September 06th, 2019

Today’s work

1. Self-study for Gaussian Process
   • Multivariate Normal Distribution
   • Cholesky Decomposition - Generating Multivariate Normal Random Number
   • Bayesian Optimization (Fully understanding this is final goal)

Since I am going to tune hyperparameter of GBM like LightGBM or Catboost for the Kaggle Competition that I am now in, I’d like to fully understand how the Bayesian Optimization. While studying these, I have been learning more detail of matrix decomposition and Gaussian Distrubiton. I’ve also been realizing that how important they are in machine learning.

2. New Posts
   • Schur Complement
   • Cholesky Decomposition

A hard one for these posts is that I wasn’t sure how deeply I should study for them. Both of Schur Complement and Cholesky Decomposition are widely and significantly used in statistics.

2019년 9월 6일

오늘 할 일

1. Gaussian Process 셀프 스터디
   • Multivariate Normal Distribution
   • Cholesky Decomposition - Generating Multivariate Normal Random Number
   • Bayesian Optimization (Fully understanding this is final goal)

Gaussian Process 를 공부하게 된 계기는 현재 참여하고 있는 Kaggle 대회에서 LightGBM 이나 Catboost 알고리즘을 사용하려 하는데, 여기서 Hyperparameter tuning을 Random Search 나 Grid Search 가 아닌 Bayesian Optimization을 사용하기에 공부하기 시작했다.
공부하다보니 가장 기본이 되는 Matrix Decomposition, Gaussian Distribution등에 대해 더 자세히 배우고 있으며, 이들이 머신러닝에서 얼마나 많이 사용되며 중요한지 배우게 되었다.

2. 새로운 포스팅
   • Schur Complement
   • Cholesky Decomposition

이번 포스팅중 어려웠던 점은 Schur Complement과 Cholesky Decomposition은 Matrix 를 다루는 분석 혹 연구에서 매우 중요하게, 그리고 많이 쓰이기에 그 개념들을 얼마나 깊게 공부해야 하는지였다. 결국 다른 공부를 할때에도 그 개념들은 이어질테니 그때 다시 더 깊게 공부해보고 싶다.