TildAlice – Tech Blog for Developers
Python, AI, Deep Learning, Data Analysis, Computer Vision, Reinforcement Learning โ tips and tutorials
Latest Posts
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Digital Twin ๊ธฐ๋ฐ ์์ง์ ๋น ์์คํ ๊ตฌ์ถ: ์ผ์ ์ตํฉ๊ณผ ์ค์๊ฐ RUL ์์ธก ํ์ดํ๋ผ์ธ ์ค๊ณ
Digital Twin predictive maintenance: fuse 12 sensor streams into real-time RUL predictions. Architecture diagram and failure detection code.
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Sim-to-Real Transfer ์์ ๊ฐ์ด๋: ์๋ฎฌ๋ ์ด์ ๊ฐํํ์ต์ ์ค์ ๋ก๋ด์ ์ ์ฉํ๋ ๋ฐฉ๋ฒ
Sim-to-Real transfer: train robot policies 100x faster in simulation, deploy to hardware with 85% success rate. Domain randomization code.
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Transfer Learning์ผ๋ก ์๋ ๋ฐ์ดํฐ ๊ณ ์ฅ ์ง๋จ ๊ตฌํ
Transfer Learning on 50 images: fine-tune ImageNet CNNs for 94% fault detection accuracy. Beats training from scratch with 1000+ samples.
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ํฌํธํด๋ฆฌ์ค ์์ฑ ๊ฐ์ด๋: Kaggle ํ๋ก์ ํธ๋ฅผ ์ค๋ฌด ๊ฒฝํ์ผ๋ก ์ดํํ๋ ๋ฐฉ๋ฒ
Turn Kaggle projects into job offers: structure your portfolio to show business impact, not just accuracy scores. Real hiring manager tips.
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A/B ํ ์คํธ ๋ถ์ ์ค์ต: ํต๊ณ์ ๊ฒ์ ์ผ๋ก ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์์ฌ๊ฒฐ์ ๋ด๋ฆฌ๊ธฐ
A/B test statistical power: detect 5% conversion lift with 95% confidence. Python code for sample size, p-values, and effect size analysis.
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์๊ณ์ด ๋งค์ถ ์์ธก ๋ถ์: Prophet๊ณผ ARIMA๋ก ๋น์ฆ๋์ค ์ธ์ฌ์ดํธ ๋์ถํ๊ธฐ
Prophet vs ARIMA for sales forecasting: MAPE comparison on seasonal retail data. When each model wins and why (with prediction intervals).
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๊ณ ๊ฐ ์ดํ ์์ธก ํ๋ก์ ํธ: ์ค๋ฌด์์ ์ฐ์ด๋ ๋จธ์ ๋ฌ๋ ํ์ดํ๋ผ์ธ ๊ตฌ์ถํ๊ธฐ
Customer churn pipeline: handle class imbalance, engineer retention features, and deploy XGBoost with 0.87 ROC-AUC. Production-ready code.
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EDA ๋ง์คํฐํ๊ธฐ: ํ์ดํ๋ ๋ฐ์ดํฐ๋ก ๋ฐฐ์ฐ๋ ํ์์ ๋ฐ์ดํฐ ๋ถ์ ์ค์ ๊ธฐ๋ฒ
EDA beyond histograms: find the 3 Titanic variables that predict survival better than passenger class. Correlation heatmap + interaction plots.