전체 글23 Summary of Linear Algebra 3.2 Norm, Dot Product, and Distance in Rnnorm : length of vector ||v||Standard Unit vector : vector of 1 length 1/||v||*vdistance between u and v : ||u-v||Dot Product : Inner Product! > u • v = ||u|| ||v|| cosß (0 ≤ ß ≤ π)u • v > 0 : acute (less than 90˚)u • v u • v = 0 : 90˚u • v = u1v1 + u2v2Euclidean inner productu • v = uvT = vuTcosß = u • v / ||u|| ||v|| 3.3 Orthogonalityorthogonal Projec.. 2024. 6. 9. Summary of Regression Analysis Matrix approach to regression analysis1. Random vectors and matrices- Mean vector- Covariance matrix : Symmetrix matrix [Basic theorems]w=Ay- A : constant matrix- y : random vector(1) E(w) = E(Ay) = A*E(y)(2) Cov(w) =A * Cov(y) * At 2. Simple linear regression model in a matrix termsy X b e- E(e)=0- Cov(e)= σ2I e~MVN(0, σ2I) 3. LSE of ßß = (XtX)-1(Xty), if (XtX)-1 exists 4. Fitted values and res.. 2024. 6. 7. Korean Data Analytics Certification : ADsP(Advanced Data Analytics Semi-Professional) Congratulations to me! I recently passed the ADsP (Advanced Data Analytics Semi-Professional) certification exam in Korea. The passing score is 60, but I managed to score a solid 80! The exam covers the following areas:- Database Concepts- Data Scientist Capabilities- Data Analytics Planning & Strategy- Data Analysis Methodologies REVIEWPassing this exam gave me a solid foundation in the core .. 2024. 6. 4. Enhance K-Means Clustering Performance with PCA! / K-평균 알고리즘에 PCA 활용하기! While K-Means Clustering is a popular choice, it can sometimes struggle with high-dimensional datasets. PCA can be a valuable tool in such cases, as it helps us focus on the most important factors and improve the algorithm's efficiency. As can be seen in the figure above, K-Means Clustering wasn't able to effectively group data points in my dataset. To address this, I implemented Principal Compo.. 2024. 5. 29. 이전 1 2 3 4 ··· 6 다음
