What is the translation of " 线性代数 " in English?

• linear algebra

Examples of using 线性代数 in Chinese and their translations into English

SystemML包含线性代数原语，统计功能和ML指定结构，可以更容易也更原生的表达ML算法。
SystemML contains linear algebra primitives, statistical functions and ML specifying structures that can be more easily and more native to the ML algorithm.

无论如何，SciPy包含更多功能更全版本的线性代数模块，以及许多其他数值算法。
In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms.

Torch的一些主要功能包括N维数组，线性代数例程，数值优化例程，高效GPU支持以及对iOS和Android平台的支持。
Some of torch's key features include N-dimensional arrays, linear algebra routines, numeric optimization routines, efficient GPU support, and support for iOS and Android platforms.

但是，由于线性代数是一种连续而非离散的数学，因此，很多计算机科学家都不太了解它。
However, because linear algebra is a form of continuous rather than discrete mathematics, many computer scientists have little experience with it.

请注意，你需要微积分和线性代数的基础知识来理解这些课程中的一些材料。
Please note that you need basic knowledge of calculus and linear algebra to understand some of the materials in those courses.

此外，它整合了丰富的功能，比如N维数组、线性代数例程以及对Android和iOS平台的高效GPU支持等。
Furthermore, it encapsulates enriched features like N-dimensional arrays, linear algebra routines, efficient GPU support for Android and iOS platforms, etc.

线性代数也用于大多数科学和工程领域，因为它允许对许多自然现象进行建模，并使用这些模型进行有效计算。
Linear algebra is also used in most sciences and engineering areas, because it allows modeling many natural phenomena, and efficiently computing with such models.

Grin是一个详尽的框架，用于在Java和Scala中完成机器学习、NLP、线性代数和可视化框架。
Grin is an exhaustive framework for completing machine learning, NLP, linear algebra, and visualization framework in Java and Scala.

正如在他们的网站上列出的那样，Torch使用者们的突出特性就是“强大的n维数组;线性代数的例程;
Some of Torche's prominent features, as listed on their website, are"a powerful N-dimensional array; linear algebra routines;

除了随机矩阵理论技术的软件，他也知道他在数值线性代数和数值计算的贡献。
Besides random matrix theory technical software, he is also known for his contributions in numerical linear algebra and numerical computing.

重点将是直觉和实际的例子，而不是理论成果，但与概率，统计和线性代数的一些经验，将是重要的。
The emphasis is on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra is important.

计算机科学家和传统的工程师需要说同样的语言-一门来源于实时分析，线性代数，概率论和物理的语言。
Computer scientists and traditional engineers need to speak the same language- a language rooted in real analysis, linear algebra, probability and physics.

去年，在他大二的时候，他带着最高的数学课提供，线性代数和多变量微积分，高级主题。
Last year, during his sophomore year, he took the highest math class offered, Linear Algebra and Multivariable Calculus or Senior Topics.

你将看到这些技术如何应用于基本问题，如排序、树和图形上的搜索，以及线性代数等。
You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others.

如何准备：要获得这个研究生级的认证，考生必须参加统计或概率、线性代数和计算机编程的入门课程。
How to prepare: To pursue the graduate certificate, candidates must have taken introductory courses in statistics or probability, linear algebra, and computer programming.

多年来，物理学家一直使用线性代数中的重要定理来快速计算最复杂问题的解。
For years physicists have used important theorems in linear algebra to quickly calculate solutions to the most complicated problems.

我们会尽可能让所有课程内容独立，但是了解线性代数和数字信号处理的基本知识将会大有用处。
We plan to make all the lectures as self-contained as possible, but basic background in linear algebra and digital signal processing will be helpful.

如果你熟悉线性代数，概率和统计，并具有编程经验，本书是你对数据科学的理想介绍。
If you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

如果你学过线性代数，微积分，概率论和编程，我建议你从斯坦福大学的CS231n课程开始。
If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n.

虽然机器学习中还有很多线性代数知识，但这篇文章提供了关于最核心的概念的一些适当介绍。
Although there are also other parts of Linear Algebra used in Machine Learning, this post gave you a proper introduction to the most important concepts.

拓扑和线性代数的结构可以更紧密地连接在一起，提供额外的假设，下面列出其中最常见的。
The topological and linear algebraic structures can be tied together even more closely with additional assumptions, the most common of which are listed below.

如果你熟悉线性代数、概率论和统计学并且具备编程经验，那么这本书就是绝佳的数据科学介绍读本。
If you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

线性代数，我们建议从Essenceoflinearalgebra系列开始学习，接着是GilbertStrang的书籍和视频。
For linear algebra, we suggest starting with the Essence of linear algebra video series, followed by Gilbert Strang's book and video lectures.

一些线性代数、微积分和Python编程语言的知识将帮助您理解本书所涵盖的概念。
Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

这很可能是电影数学谈论效果的一个例子，因为特征值是线性代数中相当低级（基本）的概念。
This is most likely an example of movie math talk for effect, as eigenvalues are a fairly low-level(basic) concept in linear algebra.

SparkMLlib是用Scala编写的，并使用线性代数包Breeze。
Spark MLlib is written in Scala, and uses the linear algebra package Breeze.

由于数组操作与等效的数学运算相似，因此线性代数的基本知识是强制使用matlab的必要条件。
Because arrays operations are so similar to the equivalent mathematical operations, a basic knowledge of linear algebra is mandatory to use matlab effectively.

在线性代数与泛函分析中，一个线性算子L的核（kernel）是所有使L(v)=0的运算对象。
In linear algebra and functional analysis, the kernel of a linear operator L is the set of all operands v for which L(v)= 0.

在线性代数、泛函分析和数学的相关领域，拟范数与范数类似，它满足范数公理，除了三角不等式被替换为.
In linear algebra, functional analysis and related areas of mathematics, a quasinorm is similar to a norm in that it satisfies the norm axioms, except that the triangle inequality is replaced by.