Ruslan Senatorov
@ruslansenatorovFounder @SENATOROVAI; Programming teacher. ITMO CT masters student
Language Breakdown
Lines of code distribution across 8 owned repositories
I-Shaped Developer
I-shapedSpecialist — deep expertise in Jupyter Notebook
Collaboration Network
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Repos
192
PRs
0
Growth
+18%
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Coding Streak
Contribution activity over the past year
Top Repositories
for simple arithmetic operation
All Algorithms implemented in Python
Multiple Linear Regression (MLR) models the linear relationship between a continuous dependent variable and two or more independent (explanatory) variables. Using the equation, it predicts outcomes based on multiple factors. Key assumptions include linearity, constant variance of residuals, and low correlation between independent variables.Solver
Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data reduction, noise removal, and matrix approximation.Solver
Cholesky decomposition is a matrix factorization method that decomposes a symmetric, positive-definite matrix into the product of a lower triangular matrix and its transpose (i.e., ). LU decomposition for solving linear equations and is widely used in Monte Carlo simulations, Kalman filters, and econometrics. Solver
The Conjugate Gradient (CG) method is an efficient iterative algorithm for solving large, sparse systems of linear equations where the matrix is symmetric and positive-definite. It finds the minimum of a quadratic function by generating conjugate search directions, ensuring convergence in at most steps for an matrix.Solver
LSQR is an iterative method for solving large, sparse, linear systems of equations and linear least-squares problems, including under- or over-determined and rank-deficient systems. It uses the Lanczos bidiagonalization process to provide a robust alternative to conjugate gradients, offering better numerical stability. Solver
Underfitting and overfitting are critical concepts in machine learning, particularly when using Polynomial Regression to model data. Polynomial regression allows a model to learn non-linear relationships by increasing the polynomial degree (e.g. ), making it highly susceptible to both underfitting (too simple) and overfitting (too complex).Solver
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 to the loss function. It forces weights to be small but rarely zero, resulting in a smoother, more stable model. Solver
Open Source Impact
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