728x90 반응형 Mathematics24 Calculus for Machine Learning and Data Science (4) Derivatives and OptimizationQuizQ1Consider the following lines.What can be said about the slopes at their intersection?Slope(Line 1) > Slope(Line 2).Slope(Line 1) Slope(Line 1) = Slope(Line 2).It is impossible to infer anything from the given information.Answer더보기2Correct! Line 2 is steeper than Line 1, therefore its slope is higher.Q2Given the following graph, what is the slope of the line? You.. 2024. 8. 25. Calculus for Machine Learning and Data Science (3) Derivatives and OptimizationDerivativesExistence of the derivativeFunctions where we cannot find the derivative at every point are called the non-differentiable functions.더보기x = 1Correct. There is a cusp at x = 1 so the derivative does not exist.더보기x = -1Correct. There is a discontinuity/jump at x=-1, so the derivative does not exist.Vertical tangents are not differentiable at the origin (x = 0).. 2024. 8. 24. Calculus for Machine Learning and Data Science (2) Derivatives and OptimizationDerivativesSome common derivatives - LinesHere are some of the common derivativesSome common derivatives - QuadraticsFor the above slide, plug in the values for $\Delta x$ (the change in $x$) and $x$, where $x = 1$ to get $\Delta f$ and then the slopeSome common derivatives - Higher degree polynomialsSome common derivatives - Other power functionsFor all power functio.. 2024. 8. 23. Calculus for Machine Learning and Data Science (1) Derivatives and OptimizationAfter completing this course, you will be able to:Learning ObjectivesPerform gradient descent in neural networks with different activation and cost functionsVisually interpret the differentiation of different types of functions commonly used in machine learningApproximately optimize different types of functions commonly used in machine learning using first-order (grad.. 2024. 8. 22. Calculus for Machine Learning and Data Science (0) What you'll learnAnalytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradientsApproximately optimize different types of functions commonly used in machine learningVisually interpret the differentiation of different types of functions commonly used in machine learningPerform gradient descent in neural networks with different act.. 2024. 8. 21. Linear Algebra for Machine Learning and Data Science (17) Determinants and EigenvectorsCheck your knowledgeQ1If $\text{det } M = 20$ and $\text{det } N = 10$, and $M, N$ have the same size. What is the value of $\text{det } M\cdot N$ and $\text{det}(N^{-1})$? (Videos: Determinant of a product, Determinant of inverses)Answer더보기$\det M\cdot N$ is 200 and $\det(N^{-1})$ is 1/10 or 0.1Q2Does the set $\{(1,2),(4,8)\}$ form a base for $\mathbb{R}^2$? (Videos.. 2024. 8. 20. 이전 1 2 3 4 다음 728x90 반응형
}