在 中文 中使用 线性代数 的示例及其翻译为 英语
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即使你没被要求，你的面试官可能会问你一些基本的多变量微积分或线性代数问题，因为它们构成了很多这些技术的基础。
Your interviewer may ask you some basic multivariable calculus or linear algebra questions, since they form the basis of a lot of these techniques.计算机科学家和传统的工程师需要说相同的语言--一种扎根于实分析、线性代数、概率论与物理学的语言。
Computer scientists and traditional engineers need to speak the same language- a language rooted in real analysis, linear algebra, probability and physics.为了测量应用程序的性能，研究人员选择了7种不同的基准测试，包括物理模拟，机器学习，线性代数，医学成像，图形着色和社交媒体。
To measure application performance, the researchers chose seven different benchmarks that spanned physics simulations, machine learning, linear algebra, medical imaging, graph coloring, and social media.它来自numpy包的线性代数模块。
It comes from the handy linear algebra module of numpy package.您应该熟悉基本的线性代数，Python和JupyterNotebook编辑器。
You should be familiar with basic linear algebra, Python and the Jupyter Notebook editor.在线性代数中，n×n单位矩阵I具有等于Kroneckerdelta的条目：.
In linear algebra, the n× n identity matrix I has entries equal to the Kronecker delta.因此，NumPy包含一些线性代数函数，即使它们更恰当地属于SciPy。
Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy.Maple针对小型和大型问题都提供了高效且全面的线性代数支持。
Maple has efficient and comprehensive linear algebra support for both small- and large-scale problems.你将学习在Python中执行线性代数，并通过使用推论统计进行分析。
You will learn to perform linear algebra in Python and make analysis by using inferential statistics.线性代数，科学，物理，更有，今天讲到的诸如化学，生物学。
I have linear algebra, science, physics, even nowadays, chemistry and biology, as you have seen.至少，你应该掌握概率和统计、多变量微积分和线性代数。
At a minimum, you should have covered probability and statistics, multivariable calculus, and linear algebra.它提供了硬件加速线性代数运算操作，以及eagerAPI实现自动微分服务。
It provides hardware-accelerated linear algebra operations and an eager API for automatic differentiation.Anandkumar说，在深度学习系统中的当前计算都是基于线性代数的。
Anandkumar says the current computations in deep learning systems are all based on linear algebra.这样的背景下应包括信号与系统课程，线性代数，科学规划和概率统计。
That background should include coursework in signals and systems, linear algebra, scientific programming, and probability and statistics.基础概念需要基本的线性代数，微积分，Python和像CS231n的一些讲座。
The underlying concepts require basic linear algebra, calculus, Python and some lectures from CS231n.最起码，你应该学过概率和统计、多变量微积分和线性代数。
At a minimum, you should have covered probability and statistics, multivariable calculus, and linear algebra.学生将继续开发他们解决问题的策略，而更深入的线性代数的概念探索。
Students will continue developing their problem solving strategies while exploring more in-depth linear algebra concepts.通常，统计学工具和线性代数可以以某种方式应用于其中的每一个问题。
Generally, statistics and linear algebra can be employed in some way for each of these questions.通常，统计和线性代数可以通过某种方式应用于这些问题中的任何一个。
Generally, statistics and linear algebra can be employed in some way for each of these questions.一般来说，统计学和线性代数都可以用某些方式用在这些问题上。
Generally, statistics and linear algebra can be employed in some way for each of these questions.
