When building a machine learning model, the types of data used can vary widely. One approach to handling this variety
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[Python] Jupyter Notebook and Jupyter Lab Shortcuts Guide
Jupyter Notebook and Jupyter Lab are essential tools for data scientists and developers. Using shortcuts in these environments can significantly
Continue readingRetrieval-Augmented Generation(RAG): 강력한 지식 기반 응답 생성을 위한 AI 기술 소개
현대의 인공지능(AI) 모델은 사용자 질문에 대한 정확하고 풍부한 답변을 제공하기 위해 끊임없이 발전하고 있다. 그 중 하나의 혁신적인 접근법이 바로
Continue readingBig Data AI Training: Efficient Methods for Loading and Processing Large Datasets
Modern AI models rely heavily on large volumes of data for accurate predictions and performance. However, loading and preprocessing large
Continue readingUnderstanding and Applying Bayes’ Theorem: A Fundamental Concept in Data Science
Today, we delve into one of the fundamental theories in data science: Bayes’ Theorem. This theorem provides a powerful framework
Continue reading빅데이터 AI 학습: 효율적인 대용량 데이터 로드와 처리 방법
현대의 인공지능(AI) 모델은 정확한 예측과 성능을 위해 대용량의 빅 데이터로 학습하는 것이 중요하다. 그러나 대용량 데이터를 불러오고 전처리하는 데는 상당한
Continue readingPitfalls of Statistics – Simpson’s Paradox
The Subtleties of Statistics Statistics uniquely deal with uncertainty and randomness, distinguishing it sharply from other mathematical topics that are
Continue readingFederated Learning (Collaborative Learning)
What is Federated Learning? Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings
Continue readingAI and Big Data Era’s Data Search Solution: Google Dataset Search
Google Dataset Search Google Dataset Search is a search engine from Google that helps researchers locate online data that is freely available for
Continue readingRandom Forest Vulnerabilities – Extrapolation
Random Forest Vulnerabilities Random Forests have the advantage of performing well without the need for extensive hyper-parameter tuning, as long
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