Data Science 썸네일형 리스트형 Probability & Statistics for Machine Learning & Data Science (1) Introduction to Probability and Probability DistributionsIn this week, you will learn about the probability of events and various rules of probability to correctly do arithmetic with probabilities. You will learn the concept of conditional probability and the key idea behind Bayes’ theorem. In lesson 2, we generalize the concept of probability of events to a probability distribution over random .. 더보기 Probability & Statistics for Machine Learning & Data Science (0) What you'll learnDescribe and quantify the uncertainty inherent in predictions made by machine learning modelsVisually and intuitively understand the properties of commonly used probability distributions in machine learning and data scienceApply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problemsAssess the perform.. 더보기 Calculus for Machine Learning and Data Science (11) Optimization in Neural Networks and Newton’s MethodNewton’s MethodNewton's MethodNewton’s method is an alternative to the gradient descent.In principle, Newton’s method is used to find the zeros of a function (where f(x) = 0).This is simply the formula of subtracting the previous point $x$ from the slope we calculate.Since Newton’s method is to find a zero of a function (f), it behaves like the .. 더보기 Calculus for Machine Learning and Data Science (10) Optimization in Neural Networks and Newton’s MethodQuizQ1Given the single-layer perceptron described in the lectures:What should be replaced in the question mark?$w_1w_2+x_1x_2+b$$w_1x_1+w_2x_2+b_1+b_2$$w_1x_1+w_2x_2+b$$w_1x_2+w_2x_1+b$Answer더보기3Correct! In a single-layer perceptron, we evaluate a (weighted) linear combination of the inputs plus a constant term, representing the bias!Q2For a Reg.. 더보기 Calculus for Machine Learning and Data Science (9) Optimization in Neural Networks and Newton’s MethodOptimization in Neural NetworksRegression with a perceptronPerceptron can be seen as a linear regression, where inputs are multiplied with weights. We output a prediction using the formula wx + b and optimize the weights (w) and bias (b).We can think of a perceptron as a single node that does the computation/calculation.Regression with a percept.. 더보기 Calculus for Machine Learning and Data Science (8) Gradients and Gradient DescentGradient DescentOptimization using Gradient Descent in one variable - Part 1It’s hard to find the lowest point using the formula above.So we can try something different by trying points in both directions and then choosing a point that’s lower than the other 2.But this also takes a long time.The lecturer introduces a term, called the "learning rate", denoted by $a$ .. 더보기 Calculus for Machine Learning and Data Science (7) Gradients and Gradient DescentQuizQ1Given that $f(x,y) = x^2y+3x^2$, find its derivative with respect to $x$, i.e. find ${\partial f\over \partial x}$Answer더보기$2xy+6x$Q2Given that $f(x,y)=xy^2+2x+3y$, its gradient, i.e. $\nabla f(x,y)$ is:$\left[\begin{array}{c} 2xy+3\\ y^2+2 \end{array}\right]$$\left[\begin{array}{c} 2xy\\ 2x+3 \end{array}\right]$$\left[\begin{array}{c} y^2+2\\ 2xy+3 \end{array.. 더보기 Calculus for Machine Learning and Data Science (6) Gradients and Gradient DescentGradientsIntroduction to Tangent planesJust like we had a tangent line in a 1-dimensional line by computing derivatives, we have a tangent plane, which is a slope or a derivative of the functions with multiple variables.To find the tangent plane we use gradient descent to speed up the optimization.A tangent is a straight line or plane that touches a curve or curved .. 더보기 이전 1 2 3 4 5 6 7 8 다음
