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1. stochastic-average-gradient-sag-saga-solver-course stochastic-average-gradient-sag-saga-solver-course Public

   Forked from SENATOROVAI/stochastic-average-gradient-sag-saga-solver-course

   The SAG (Stochastic Average Gradient) + SAGA (Accelerated) solver is an optimization algorithm used primarily in machine learning, specifically for logistic regression and linear support vector mac…

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2. L2-regularization-ridge-regression-course L2-regularization-ridge-regression-course Public

   Forked from SENATOROVAI/L2-regularization-ridge-regression-course

   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 weig…

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3. underfitting-overfitting-polynomial-regression-course underfitting-overfitting-polynomial-regression-course Public

   Forked from SENATOROVAI/underfitting-overfitting-polynomial-regression-course

   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 relatio…

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4. LSQR-solver-course LSQR-solver-course Public

   Forked from SENATOROVAI/LSQR-solver-course

   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 Lan…

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5. conjugate-gradient-sparse-cg-solver-course conjugate-gradient-sparse-cg-solver-course Public

   Forked from SENATOROVAI/conjugate-gradient-sparse-cg-solver-course

   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 minimu…

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6. gradient-descent-sgd-solver-course gradient-descent-sgd-solver-course Public

   Forked from SENATOROVAI/gradient-descent-sgd-solver-course

   Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model parameters iteratively using small, random subsets (batches) of data, rather than the entire dataset. It significan…

   Jupyter Notebook 1