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[ru] cs-229-deep-learning, cs-229-supervised-learning #21

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[ru] cs-229-deep-learning, cs-229-supervised-learning #21

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ENH: neural nets
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22 changes: 11 additions & 11 deletions ru/cheatsheet-supervised-learning.md
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**2. Introduction to Supervised Learning**

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⟶ Введение в обучение в подкреплением
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Supervised learning in Russian is "Обучение с учителем".


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**4. Type of prediction ― The different types of predictive models are summed up in the table below:**

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&#10230; Тип предсказания - Различные типы предсказывающих моделей перечислены в таблице ниже:

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**5. [Regression, Classifier, Outcome, Examples]**

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&#10230; [Регрессия, классификатор, вывод, примеры]

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**7. Type of model ― The different models are summed up in the table below:**

&#10230;
&#10230; Тип модели - Различные типы моделей перечислены в таблице ниже:

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**10. Notations and general concepts**

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&#10230; 10. Обозначения и основные понятия

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**11. Hypothesis ― The hypothesis is noted hθ and is the model that we choose. For a given input data x(i) the model prediction output is hθ(x(i)).**

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&#10230; Гипотеза - Гипотеза, обозначаемая как hθ, является выбранной нами моделью. Для входных данных x(i) предсказания модели будут обозначаться hθ(x(i)).

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**21. Linear models**

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&#10230; Линейные модели

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**22. Linear regression**

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&#10230; Линейная регрессия

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**23. We assume here that y|x;θ∼N(μ,σ2)**

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&#10230; Положим, что y|x;θ∼N(μ,σ2)

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**28. Classification and logistic regression**

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&#10230; Классификация и логистическая регрессия

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**33. Generalized Linear Models**

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&#10230; Обобщенные линейные модели

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