rudgh99

[Expression_3] despite of / In spite of / Although

rudgh99_algo — Sat, 18 Oct 2025 06:10:19 +0900

You can use these expressions when you want to show contrast between two ideas.

despite + noun
in spite of + noun
although + subject + verb

For example,

Despite the weather, I went to the beach.
In spite of the traffic, I took the subway.
Although I failed the exam, I kept studying English.

[Expression_2] on top of that / on top of all those things

rudgh99_algo — Wed, 15 Oct 2025 23:03:37 +0900

If you are preparing for the OPIc test, I highly recommend this expression.

This expression means “게다가.”

You can also use a slightly different version, such as “On top of all those things.”

This means “그 모든 것들 외에도.”

For example:

On top of that, they have scrumptious food.
On top of all those things, they usually have great service as well.

[Expression_1] It seems like

rudgh99_algo — Wed, 15 Oct 2025 22:14:46 +0900

If you are preparing for the OPIc test, you can use this expression.
This expression means “~인 것 같다.”

For example,

It seems like most people here prefer taking the subway.
It seems like getting a job is really difficult for everyone.
It seems like it will rain tomorrow.

[English] 표현 모음집

rudgh99_algo — Sat, 20 Sep 2025 08:26:23 +0900

1. think of : 생각나다. I can't think of his names.

2. I lost track of time : 시간 가는 줄 몰랐다.

3. I just wasted my money. 나 그냥 돈 날렸어.

4. all that : 그다지 they aren't all that attractive.

5. The only thing that remains is a picture.

6. I can't leave out the people who are familiar with me. 나랑 친한사람을 제외할 수 없다. I can't leave out abs either.

7. I feel like my stomach is getting more defined. 내 복부 근육이 더 선명해지는 것 같아.

8. I don't think today is possible.

9. Today doesn't work for me.

10. Are there any discounts or promotions available?

11. What's the best price you can give me.

12. Does friday at 5pm work for you?

13. What are your business hours?

14. I didn't recognize him but I pretended to remember him.

15. on top of that ; 게다가 ; On top of that, I've spent too much money. I've spent a lot of money.

16. end up ing ; I just ended up spending too much money.

17. The other day ; 저번에

18. spending time alone

19. Are you done? : 끝났어?

20. I'm done with my hair cut.

21. Hey, notice anything? ; 나 달라진 거 없어?

22. I think that pretty much sums it up. ; 그거면 다 요약 되겠네요.

23. You should take it up with him/HR/her. ; 너가 him/her/HR가 따지면 되겠네.

24. I've got butterflies in my stomach. : 긴장되다.

[diary] 2025.09.12

rudgh99_algo — Fri, 12 Sep 2025 19:29:20 +0900

Today is the first day that I write down my diary, so give me applause!!

On the weekend, I usually stay at home and not go out. It’s because I have lots of things to do at home.

I usually study English to be a good English speaker, and I also work out at the gym. I worked out my legs today. It’s always pretty hard during the workout, but it also refreshes me.

Oh, I forgot to write that I take medicine at 6:00 a.m. to prevent hair loss.

And then I always drink some coffee. Without coffee, I’m just gonna die.

Anyway, I thought about lots of things today. Especially, I’m confused about what I should do.

Maybe Quality Assurance, AI … I don’t know what I like.

If I choose AI, I have to study linear algebra, probability & statistics, and calculus. And if not, I will just submit my resume to corporations and add some certificates.

Maybe, guys, I’m going to pick the choice by tomorrow.

See you tomorrow.

[English] get,have,take, do, make, go , be

rudgh99_algo — Wed, 10 Sep 2025 23:36:10 +0900

1. get ; 없다가 생기는 뉘앙스

ex) I get a ticket. I get a table. I get a job.

2. have : 완전히 내 것이 되었다.

ex) I don't have it -> I get it. -> I have it.

ex) I have a job(직업이 있다.) , 취직하다 get a job

ex) I have a cold , I get a cold .

ex) I have breakfast

3. take : 손을 뻗어서 갖고오다, 노력

ex) I take my medicine. I take a shower. I take a picture.

4. do : 일상적으로 자주 하다.

ex) I do the housework. I do my homework. I do yoga.

5. make : 어쩌다 한번 만들어내는 사건

ex) I make a mistake. I make a lot of money.(I have a lot of money.) I make a friend. ( I have a lot of friends.)

6. go : 어디론가 이동하다.

ex) go to school.

ex) go to the drugstore(맨날 가는 곳이 아님)

ex) go for dinner

7. be : 가만히 있는 주어의 상태

ex) get married(미혼에서 기혼이 되다.) , 기혼이다 (be married, i'm married)

ex) get cold --> I'm cold.

ex) get hungry(배고파졌다.) --> be hungry(배고프다.)

[English] It has been. It's been.

rudgh99_algo — Wed, 10 Sep 2025 22:52:36 +0900

1. How long has it been? 얼마나 됐어?

It's been almost 6 months. It's been a long time. It's been many years.

It hasn't been very long.

2. How has it been? 과거+현재동안 어땠어요? (How is it?;어때요?)

It's been kind of tough. ( 좀 힘들었어.) , It's been fun.

It's been great so far. (so far(여태까지는), 여태까지는 좋아요.)

It's been really helpful. Thanks again.

It's been getting better / worse. (나아지고 있어요.)

It's been improving.

It's been snowing all morning.

It's been + p.p --> It's been canceled / delayed / pushed back / fixed / taken care of / stolen / renewed / renovated several times / closed permanently. (It was canceled랑은 틀림.)

https://www.youtube.com/watch?v=qXJEQzI9U-k

[유용한 표현] 회화1

rudgh99_algo — Sat, 30 Aug 2025 12:38:51 +0900

1. I'm going to have to 동사원형 --> 어쩔 수 없이 동사원형 해야겠다.

ex. I'm going to have to decline. I'm going to have to let you go. We are gonna have to fire you.

2. In terms of --> ~ 측면에서

ex. In terms of distance, I prefer japan because it's just two-hour flight.

ex. In terms of cost, I think this method would be good.

3. be supposed to 동원 --> ~하기로 되어있다.

ex. You're supposed to be class in time.

ex. I'm supposed to meet my boss for a meeting at 2pm.

ex. You're not supposed to forget this method. Aren't you?

4. Feel free to 동원 --> 언제든지, 얼마든지 ~ 해

ex. Feel free to look around.

ex. Feel free to call me anytime.

ex. Fell free to call anytime.

5. Fee like 명사/동명사 --> ~가 땡긴다. ~하고 싶은 기분이다.

ex. I feel like some breakfast.

ex. I feel like some paper. I feel like some that materials.

ex. I feel like getting some lunch.

ex. I feel like going out tonight.

ex. I feel like you need some outside help.

6. What + S + V --> ~하는 것, ~하는 일

ex. That's what i need.

ex. What you said yesterday was disappointing.

7. get used to 명사/동명사 --> ~에 익숙해지다. (익숙한 상태가 되는 과정)

ex. I'll get used to this method.

ex. I'm getting used to this method.

8. there is/are --> 있다, 존재하다.

ex. There is something new techniques.

ex. There are some new materials.

ex. There's no equipment here.

9. be into 명사/동명사 --> 아주 좋아하다.

ex. I'm really into you.

ex. I'm really into this work.

ex. What are you into these days?

10. I have something to 동사원형 --> ~ 할 것이 있다.

ex. I have something to tell you.

ex. I have something to discuss.

ex. We have something to discuss about the SOTA project.

ex. We have something to discuss.

Source :

https://www.youtube.com/watch?v=QWyyHrtxKdQ

[Linear_Algebra] Null space of Matrix and Linear system equations

rudgh99_algo — Fri, 22 Aug 2025 14:19:08 +0900

In this post, we are going to talk about the Null space and solving linear system equations by using the null space.

Null space

Let A be a matrix whose columns are [a1, a2, ..., an].

The equation is as follows:

AX = 0 ; N(A) = {X | AX = 0}.

We can interpret this intuitively: when matrix A multiplies a vector X and the result is zero, those X vectors form the null space. This means that the null space contains all X that are mapped to the zero vector by A.

From previous posts, a vector space is defined when 3 conditions are satisfied. So let’s check:

If A(x1) = 0 and A(x2) = 0, then A(k1x1 + k2x2) = k1A(x1) + k2A(x2) = 0.
This shows that the null space is a subspace.

Example 1. A = [[1,5,2],[0,4,4]]. Then the null space is N(A) = {0}, and the column space C(A) is all of R^2.

Example 2. A = [[1,5,2],[0,4,4],[1,4,4]]. Then the null space is N(A) = {k(1,1,-1)}, which is a line through the origin. The column space C(A) is a plane in R^3.

Example 3. A = [[1,1],[2,2]]. In this case, the two columns are the same.

The null space is N(A) = {k(1,-1)}, which is a line through the origin.
The column space is C(A) = span{(1,2)}, which is also a line in R^2.

Linear system equations with Null space

Unique solution: Ax = b, where A is an invertible n×n matrix.

If A^-1 exists, it means all the column vectors of A are linearly independent. So the rank of A is n, the column space C(A) = R^n, and the null space N(A) = {0}.
Because Ax = 0 → A^-1Ax = A^-1(0) → x = 0.

Non-invertible case: Ax = b.

Suppose Ax = b has infinite solutions.
In this case, we can write the general solution as x = xp + xn,
where xp is a particular solution of Ax = b, and xn is any solution of Ax = 0 (element of the null space).

Example: A = [[1,1],[2,2]].

If b is not in span{(1,2)}, i.e. b2 ≠ 2b1 → no solution.
If b2 = 2b1 → infinite solutions.

First, solve Ax = 0:
Let X = [y,z]. Then the equation gives y + z = 0 → y = -z.
So the null space is span{(1,-1)}.

Next, find a particular solution xp.
Let’s say b = [1,2]. One valid solution is y=1, z=0 → xp = (1,0).

Therefore, the general solution is
X = xp + k·(1,-1) = (1,0) + k(1,-1) = [1+k, -k].

[ML_8] Ensemble : Voting , Bagging , Boosting(+)

rudgh99_algo — Fri, 22 Aug 2025 13:45:55 +0900

Ensemble Method Definition

An ensemble method is a machine learning approach that combines multiple weak learners (individual models) to produce a stronger and more accurate predictive model.
The key idea is that by aggregating the predictions of several models, the ensemble can reduce variance, bias, or improve generalization compared to a single model.

Representative Types of Ensemble Methods

Voting
- Concept: Voting combines predictions from multiple models of different types (e.g., logistic regression, decision tree, k-nearest neighbors) that are all trained on the same dataset.
- How it works:
  - Hard Voting: Each model votes for a class label, and the class with the majority votes is selected.
  - Soft Voting: Each model outputs class probabilities, and the probabilities are averaged to make the final decision.
- Key Point: Voting is a model-agnostic ensemble technique since it can combine any type of algorithm.

#%% 
#### Voting classifier 
from sklearn.ensemble import VotingClassifier 
from sklearn.linear_model import LogisticRegression 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.model_selection import train_test_split 
from sklearn.metrics import accuracy_score 
from sklearn.datasets import load_breast_cancer 
import pandas as pd 

bc = load_breast_cancer() 
data_df = pd.DataFrame(bc.data, columns = bc.feature_names)   

#%% 
lr_ml = LogisticRegression(solver = 'liblinear') 
kc_ml = KNeighborsClassifier(n_neighbors= 8) 

vo_clf = VotingClassifier( estimators = [('LR', lr_ml), ('KNN', kc_ml)] , voting = 'soft') 

X_train, X_test, y_train, y_test = train_test_split(bc.data, bc.target, test_size = 0.2, random_state=156) 

vo_clf.fit(X_train, y_train) 
y_pred = vo_clf.predict(X_test) 
acc_score = accuracy_score(y_test, y_pred) 
print(f"voting acc_score : {acc_score}") 

classifiers = [lr_ml, kc_ml] 

for classifier in classifiers: 
    classifier.fit(X_train, y_train) 
    y_pred = classifier.predict(X_test) 
    class_name = classifier.__class__.__name__ 
    print(f"class_name : {class_name} ,acc_score : {accuracy_score(y_test,y_pred)}")

Bagging (Bootstrap Aggregating)
- Concept: Bagging uses the same algorithm (e.g., decision trees) but trains each model on a different bootstrapped sample of the data.
- Bootstrapping: Sampling with replacement, which means some data points may appear multiple times in a sample while others may not appear at all.
- How it works:
  - Each model (e.g., tree) is trained independently on its own random sample.
  - The final prediction is made by averaging (for regression) or voting (for classification) the outputs of all models.
- Example: Random Forest is a well-known bagging-based ensemble of decision trees.

Boosting
- Concept: Boosting trains models sequentially, where each new model focuses on correcting the errors of the previous models.
- How it works:
  - The first model is trained on the dataset.
  - Subsequent models are trained on data points that were misclassified or had high error, giving them higher weights.
  - Final predictions are made by combining all models with weighted voting or averaging.
- Key Intuition: Boosting “boosts” weak learners by focusing more on difficult cases.
- Examples: AdaBoost, Gradient Boosting, XGBoost, LightGBM.

Ref : https://medium.com/@chyun55555/ensemble-learning-voting-and-bagging-with-python-40de683b8ff0

Ensemble Learning — Voting and Bagging with Python

In machine learning, ensemble learning uses multiple learning algorithms in order to obtain better performance results. In classification…

medium.com

https://www.nb-data.com/p/comparing-model-ensembling-bagging

Comparing Model Ensembling: Bagging, Boosting, and Stacking - NBD Lite #7

Simple summary of the popular methodologies