TIL(Today I Learned) - C, Statistics, Data Science, ML/DL, Python, Git

C

• 1d/2d-array with pointer
• show address in 2d-array
• malloc and realloc
• linked list - make single node
• linked list - make multi node
• binary search tree - malloc and free
• size_t in C
• unsigned char in C
• void pointer in C

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NodeJS

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Data Preprocessing

• Normalization vs Standardization
• Remove Outlier (Functionalization)
• upsampling (match the number of 'y - 0,1' in classification model)
• LabelEncoder vs OneHotEncoder vs LabelBinarizer

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Pandas DataFrame

• reset_index(drop=True)
• df[col].ffill()
• pd.melt(df, id_vars, value_vars, var_name, value_name)

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Feature Engineering

• feature_selection(from sklearn) - RFE(Recursive Feature Elimination)

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Visualization

• Seaborn - basic visualization (flights dataset)

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Machine Learning (Modeling)

• K-Nearest Neighbor Algorithm
• Random Forest Classifier
• Gradient Boosting Classifier
• Hyper-Parameter Tuning, GridSearchCV(from sklearn.model_selection)
• [GridSearchCV(n_jobs=estimator=Model(), param_grid=param_grid, ... , multiprocessing.cpu_count())]
• [make_pipeline(from sklearn.pipeline)]
• XGBoost(Regression/Classification)
• lightgbm(Regression/Classification)
• basic k-means clustering
• basic linear regression

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Deep Learning (Modeling)

• [data 'class_mode' and Model Compile 'loss']
• [Conv2D(.. padding='same')]
• [Conv2D and BatchNormalization]
• Multiple Object Detection with 2 Step(R-CNN/SPP-Net/Fast R-CNN/Faster R-CNN)
• GAN - Basic Concept
• StarGAN - Basic Concept
• StarGAN v2 - Basic Concept
• [Paper] StarGAN v2: Diverse Image Synthesis for Multiple Domains - Yunjey Choi, Youngjung Uh, Jaejun Yoo, Jung-Woo Ha
• [CGAN - Conditional GAN]
• pix2pix model : CycleGAN vs StarGAN
• GAN : Unpaired Image to Image Translation
• keras basic(CNN)
• Representation learning
• type of classification and activation function

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Model Evaluation

• Explain classification_report(from sklearn.metrics) with examples
• confusion_matrix(from sklearn.metrics)
• Feature Importance vs Permutation Feature Importance
• Precision and Recall in an Anomaly Detection Situation
• ROC Curve +metrics.plot_roc_curve(model, X_test, y_test)

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Statistics

• R Square
• df.corr() | correlation coefficient - pearson, spearman, kendall tau
• scipy.stats.kstest() | Kolmogorov-Smirnov Test
• (statsmodels.api)OLS.from_formula('y ~ x1+x2+x3..', data=df)
• 범주형/연속형 변수 조합에 따른 가설 검정 방법
• ARIMA
• 중심극한정리 샘플링테스트

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R

• *Tutorial : (1) Getting Started | Rachael Tatman | Kaggle
• *Tutorial : (2) Load Data | Rachael Tatman | Kaggle
• *Tutorial : (3) Summarize Data | Rachael Tatman | Kaggle
• *Tutorial : (4) Graphing Data | Rachael Tatman | Kaggle
• *Tutorial : Introduction to machine learning in R | Cam Nugent | Kaggle

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SQL

• INNER JOIN vs OUTER JOIN
• *프로그래머스 | GROUP BY | 동명 동물 수 찾기
• *프로그래머스 | GROUP BY | 입양 시각 구하기(1)
• *프로그래머스 | GROUP BY | 입양 시각 구하기(2)
• *프로그래머스 | IS NULL | NULL 처리하기
• *프로그래머스 | JOIN | 없어진 기록 찾기
• *프로그래머스 | JOIN | 있었는데요 없었습니다
• INNER JOIN 2 Table / FROM 2 Table
• *프로그래머스 | JOIN | 오랜 기간 보호한 동물(1)
• *프로그래머스 | JOIN | 보호소에서 중성화한 동물
• *프로그래머스 | String, Date | 이름에 el이 들어가는 동물 찾기
• *프로그래머스 | String, Date | 중성화 여부 파악하기
• *프로그래머스 | Summer/Winter Coding(2019) | 우유와 요거트가 담긴 장바구니
• *프로그래머스 | 2021 Dev-Matching: 웹 백엔드 개발자(상반기) | 헤비 유저가 소유한 장소
• *LeetCode | 181. Employees Earning More Than Their Managers
• *LeetCode | 626. Exchange Seats

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Terminal & Git

• touch vs mkdir (+git status)
• branch, checkout
• [git fetch --all & git reset --hard origin/main (overwrite local branch with remote Repository)]

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Server

• System log vs User log
• Scheduling

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Markdown

• Markdown index with inner link

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Docker

• Docker Intro - quick usage