Школа Data Science Руслана Сенаторова [SenatorovAI]
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L-BFGS-B-solver-course
PublicLinear regression with the LBFGSB (Limited-memory Broyden-Fletcher-Goldfarb-Shanno BFGS) solver method is a numerical optimization method used to find the minim…intro
PublicLabs_and_VKRs
PublicVehicle-Collection-CLI
Publiclab_scada_project
PublicKinozal_VKR
PublicOpenstudio
Public- Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model parameters iteratively using small, random subsets (batches) of data, rather t…
.github
PublicPython_Yurij_Liposkij
PublicCarPrediction
PublicData-Science-For-Beginners-from-scratch-course
Public templateData science for beginners involves learning to extract insights from data using statistics, programming (Python/R), and visualization. Key steps include data c…- Deploying a FastAPI application on a Virtual Private Server (VPS) typically involves a "traditional" setup using an ASGI server like Gunicorn with Uvicorn worke…
- The primary open source standard for Python github code is PEP 8: The Style Guide for Python Code, which provides guidelines to enhance code readability and con…
- The normal equations for simple linear regression are a system of two linear equations used to find the optimal intercept and slope that minimize the sum of squ…
- Multiple Linear Regression (MLR) models the linear relationship between a continuous dependent variable and two or more independent (explanatory) variables. Usi…
- Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices co…
- Cholesky decomposition is a matrix factorization method that decomposes a symmetric, positive-definite matrix into the product of a lower triangular matrix and …
- The Conjugate Gradient (CG) method is an efficient iterative algorithm for solving large, sparse systems of linear equations where the matrix is symmetric and p…
LSQR-solver-course
PublicLSQR is an iterative method for solving large, sparse, linear systems of equations and linear least-squares problems, including under- or over-determined and ra…- Underfitting and overfitting are critical concepts in machine learning, particularly when using Polynomial Regression to model data. Polynomial regression allow…
- L2 regularization, or Ridge regression, is a technique to prevent overfitting in machine learning by adding a penalty proportional to the sum of squared weights…
- The SAG (Stochastic Average Gradient) + SAGA (Accelerated) solver is an optimization algorithm used primarily in machine learning, specifically for logistic reg…
- QR decomposition, or QR factorization, is a fundamental linear algebra method that decomposes a matrix into a product of an orthogonal matrix and an upper trian…
SenatorovAI
Publicweb-development
Publicdevops-lab-kazar
Publicmachine-learning-refined
Public