NUMPY - 翻译成英语

• numpy

在 中文 中使用 Numpy 的示例及其翻译为 英语

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Sparse矩阵而不是numpy.ndarray。
Sparse matrix by default instead of a numpy. ndarray.

Scipy是基于numpy。
SciPy is built on NumPy.

Theano是用Python，结合Numpy实现的。
Theano is Python, in conjunction with Numpy.

Theano是一个Python包，它定义了与NumPy.
Theano is Python, in conjunction with Numpy.

本书会经常用到NumPy。
This library will generally use with NumPy.

我喜欢Scipy作为Numpy的增益程序库。
I like to think of Scipy as an addon library to Numpy.

Sparse矩阵，而非numpy.ndarray。
Sparse matrix by default instead of a numpy. ndarray.

Numpy.ma模块可以用作numpy的补充：.
The numpy. ma module can be used as an addition to numpy.

默认情况下，结构化数据类型在numpy中实现为基本类型numpy.
Structured datatypes are implemented in numpy to have base type numpy..

我将会在这使用numpy.
I'm going to use here the randn function in numpy.

通过直接将掩码数组视为numpy.
By directly taking a view of the masked array as a numpy.

所有的课堂作业都将使用Python(使用NumPy和PyTorch)。
All class assignments will be in Python(with numpy.).

因此，我们将需要深入NumPy的内部。
In that respect, we will need to dig into the internals of NumPy.

IPythonnotebook和NumPy可以用于轻量工作的处理，而Python则是中级规模数据处理的有力工具。
IPython notebook and NumPy can be used as a scratchpad for lighter work, while Python is a powerful tool for medium-scale data processing.

在numpy中，有些运算返回shape为(R,1)而有些返回(R,)。
In numpy, some of the operations return in shape(R, 1) but some return(R,).

DenisYarats使用NumPy，pandas和scikit-learn进行一般机器学习：“我喜欢它们的简洁性和透明度。
Denis Yarats uses NumPy, pandas, and scikit-learn for general machine learning:“I like their simplicity and transparency.

Scikit-learn是在另外三个开源项目Matplotlib，NumPy和SciPy上设计的，它专注于数据挖掘和数据分析。
Scikit-learn is designed on three other open source projects- matplotlib, NumPy, and SciPy- and it focuses on data mining and data analysis.

由于我们将使用Numpy方法读取和写入文件，你可以在第一次阅读时跳过本章。
Since we will use the Numpy methods to read and write files, you may skip this chapter at first reading.

此外，我们的NumPy的解决方案，同时涉及的Python堆栈递归和许多临时数组的分配，这显著地增加了计算时间。
Furthermore, our NumPy solution involves both Python-stack recursions and the allocation of many temporary arrays, which adds significant computation time.