Mathematics for Machine Learning: Multivariate Calculus

开始时间: 08/01/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/multivariate-calculus-machine-learning

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This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

机器学习数学:多元演算:本课程简要介绍了构建许多常见机器学习技术所需的多元演算。我们从一开始就对斜率的“超越运行”公式进行复习,然后再将其转换为函数梯度的形式定义。然后,我们开始构建一套工具,使演算更轻松,更快捷。接下来,我们将学习如何计算指向多维表面上山丘的矢量,甚至使用交互式游戏将其付诸实践。我们将研究如何使用微积分建立函数的近似值,以及帮助我们量化期望这些近似值的精确度。在最终向您展示如何将其应用于线性回归模型之前,我们还花了一些时间讨论微积分在神经网络训练中的作用。本课程旨在提供对微积分的直观理解,以及当您陷入困境时查找自己的概念所必需的语言。希望您不必过多讨论,便会信心满满,将来继续学习一些更专注的机器学习课程。

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课程简介

This course offers a brief introduction to the multivariate calculus required to build many common m

课程标签

伦敦帝国理工学院 机器学习 数学 主成分分析 线性代数 数据科学 机器学习数学基础 面向机器学习的数学 PCA 多变量微积分 帝国理工学院 微积分

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