Mathematics for Machine Learning Practice

About this grouping

A formula may describe an activation, a loss or an update. Knowing its role and assumptions matters more than recognizing its surface pattern.

Choose what to practice

Each Start opens that topic’s available practice. This grouping does not launch a combined session or enroll you in a course.

• Machine-learning mathematical forms

  Frequently used loss, activation, probability, and optimization formulas.

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• Small two-by-two determinants

  Calculating exact determinants of two-by-two matrices.

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• Vector formulas

  Component forms for dot products, vector length, and cross products.

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• Matrix formulas

  Matrix products, 2×2 determinants and inverses, and basic matrix relationships.

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• Eigenvalue and least-squares formulas

  Eigenvector equations and least-squares relationships.

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• Multivariable operator formulas

  Standard gradient, divergence, curl, and multivariable calculus formulas.

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• Basic probability rules

  Recall complements, unions and independent intersections.

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• Expectation and variance formulas

  Expectation, variance, and second-moment relationships.

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• Covariance and correlation formulas

  Covariance, second-moment, and correlation relationships.

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• Conditional probability

  Recall probability given that another event has occurred.

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• Binomial probability

  Recall the binomial probability formula and its conditions.

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• Bayes' theorem

  Recall Bayes’ theorem with its stated conditions.

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