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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[๋ ผ๋ฌธ๋ฆฌ๋ทฐ] Mamba: Selective State Space Model๋ก Transformer์ ํ๊ณ๋ฅผ ๋ํํ๋ค
Mamba beats Transformer with linear-time complexity using Selective State Space Models. See the selection mechanism that makes it work.
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ConvNeXt vs Swin Transformer: ํ์ด๋ธ๋ฆฌ๋ ์ํคํ ์ฒ ์ค์ ์ฑ๋ฅ ๋น๊ต์ ์ต์ ์ ํ ์ ๋ต
ConvNeXt vs Swin Transformer on ImageNet, COCO, ADE20K: real accuracy and speed numbers to pick the right architecture for your project.
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์ด๋ถ ํ์(Binary Search) ์๋ฒฝ ๊ฐ์ด๋ – ์๊ณ ๋ฆฌ์ฆ ์๋ฆฌ๋ถํฐ ์ฝ๋ฉํ ์คํธ ์์ฉ๊น์ง
Master binary search from O(log n) basics to parametric search. Includes lower/upper bound implementation and coding interview patterns.
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RAG ํ์ดํ๋ผ์ธ ์ต์ ํ ์์ ๊ฐ์ด๋: Naive RAG๋ถํฐ Agentic RAG๊น์ง
Optimize RAG pipelines from naive to agentic: chunking strategies, hybrid search, and reranking code that actually improves retrieval.
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XAI๋ก ๋ฒ ์ด๋ง ๊ณ ์ฅ ์์ธ ๋ถ์ํ๊ธฐ: SHAP๊ณผ Grad-CAM์ผ๋ก CNN ์ง๋จ ๋ชจ๋ธ ํด์
Use SHAP and Grad-CAM to prove your CNN bearing diagnosis model is looking at fault frequencies, not noise. Validation workflow included.
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DeepSeek-V3 ๋ ผ๋ฌธ ๋ฆฌ๋ทฐ: MoE ๊ธฐ๋ฐ ์คํ์์ค LLM์ด GPT-4o๋ฅผ ๋์ด์ ๋น๊ฒฐ
DeepSeek-V3 matches GPT-4o with 671B parameters but activates only 37B. The MoE, MLA, and load balancing techniques that made it possible.
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UMAP vs t-SNE ์๋ฒฝ ๋น๊ต: ๊ณ ์ฐจ์ ๋ฐ์ดํฐ ์๊ฐํ ์ค์ ๊ฐ์ด๋ (Python ์ฝ๋ ํฌํจ)
UMAP vs t-SNE: speed, global structure, and hyperparameter comparison on real datasets. Python code to pick the right tool for your data.
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PySpark์ Pandas ์ฐ๋์ผ๋ก ๋ฐฐ์ฐ๋ ๋๊ท๋ชจ ๋ฐ์ดํฐ ํ์ดํ๋ผ์ธ ๊ตฌ์ถ ์ค์ ๊ฐ์ด๋
PySpark + Pandas Arrow UDF: cut ETL runtime by 80% on 100GB datasets. Real benchmark numbers and optimization patterns included.