• Python NAPALM Network Automation 中文字幕 Python NAPALM网络自动化 中文字幕Python NAPALM Network Automation 具有多供应商支持的网络自动化和可编程性抽象层（NAPALM）是一个Python库，您可以使用它来使用统一...
Python NAPALM Network Automation 中文字幕
Python NAPALM网络自动化 中文字幕Python NAPALM Network Automation
具有多供应商支持的网络自动化和可编程性抽象层（NAPALM）是一个Python库，您可以使用它来使用统一API自动化并与网络设备和操作系统进行交互 由于此库提供了抽象层，因此可以更轻松地配置多个供应商设备 在本课程中，学习如何使用NAPALM自动配置网络设备 讲师David Bombal概述了NAPALM，解释了如何将其用于交换机和BGP自动化，以及如何执行设备配置审计
主题包括： 使用NAPALM连接到交换机 将JSON与NAPALM一起使用 使用NAPALM检索BGP邻居 连接多个BGP路由器 自动化大型BGP网络 使用NAPALM配置ACL 审核您的设备配置
[Tutor]因此NAPALM用于网络自动化，但提供了一个抽象层，可以轻松配置多个供应商设备。 该软件可在GitHub上获得。 我将在稍后向您展示如何在GNS3拓扑中安装它。 支持许多网络设备，您可以在此链接上看到列表。 例如，Arista EOS，Juniper，CiscoIOS XR，Cisco Nexus，CiscoIOS以及其他供应商。 这个软件使得在python中通过合并配置，替换配置，比较配置以及必要时回滚配置来配置设备变得非常容易。 该软件使您可以轻松地编写一个非常强大的简单python脚本作为网络工程师。 举例来说，您可以在python代码中使用get_bgp_neigbors函数，无论您是连接到CiscoIOS设备，IOS XR设备，JunOS设备，Nexus设备，Arista设备还是其他受支持的设备，都会返回相同的结果。 此表中显示了相关命令的受支持供应商列表。 我马上要证明这一点，但你可以再次获得ARP表。 来自多个供应商或获取设备配置或获取环境信息。 或者在NPR环境中获取有关设备或接口或接口计数器或第三层IP地址LLTP邻居，MAC地址表信息NTP信息，VRF的事实。 您甚至可以从设备发送Pings或从这些设备发送Trace Routes并收集响应。 所以NAPALM非常强大。 因为它从低级编程中抽象出来。 抽象的想法很复杂。 例如，当您驾驶机动车时，您可以转动钥匙或按下按钮启动汽车。 （引擎咆哮）但是有很多隐藏的事情发生在汽车开始。 （发动机咆哮）你按下加速器更快。 （引擎咆哮）但是你隐藏了很多复杂性。 汽车提供了一个简单的界面，你可以看到你的速度作为一个例子，你可以看到你的加速度，你可以转动方向盘左转或右转。 为复杂的机器提供了一个非常简单的界面。 这里也有同样的想法。 您隐藏在多个供应商的低级编程接口和API之外，您可以使用简单的命令从网络设备检索信息，并使用NAPALM对网络设备进行编程。 现在一些网络设备有适当的API。 CiscoIOS没有。 因此，CiscoIOS没有您可以使用的本机API。 但那不会阻止我们。 NAPALM使用Netmiko与网络设备进行交互。 因此，Netmiko是将NAPALM与CiscoIOS设备配合使用的先决条件。 在这个GN3拓扑中，我有一个Cisco IOS第二层交换机和Cisco IOSv路由器。 我将演示如何在此Ubuntu服务器上安装和使用NAPALM来提取信息并配置这些IOS设备。 GitHub页面上显示了文档的链接。 readthedocs提供了文档，您可以在文档页面上找到类似的注意事项和先决条件。 因此，如果您需要更多信息或提出问题，请查看NAPALM文档，我会尽力回答。 但现在我将向您展示如何安装NAPALM并使用它从GNS3中运行的这些网络设备中检索信息。 本课程视频下载地址:Python NAPALM网络自动化
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• 网络流量获取器 用于获取 SNMP 网络接口信息并计算总流量和速度等统计数据的小型。 安装 pip install nettraffic 用法 去做
• Python及相关的版本号如下图所示： 3.准备数据 目的： 生成3类圆在第一象限内的坐标（圆心都是原点） 第1类：半径范围为110，分类标识为‘0’ 第2类：半径范围为1020，分类标识为‘1’ 第3类：半径范围为20~30，...
• python库API） •一键式编码概念和生成 •反向传播算法的实现，可使用SGD训练网络 •混淆矩阵（实现和解释） •“ Sigmoid”和“ relu”作为激活功能 •读者进行一次小练习，以在使用“ relu”功能时优化成本 [注意...
• 适用于Cisco NSO的Python客户端（之前为tail-f） 安装 要安装，请使用pip： $pip install pynso 或克隆仓库：$ git clone https://github.com/DimensionDataResearch/pynso.git \$ python setup.py install ...
• Python Theory for Network Engineers 中文字幕 网络工程师的Python教程 中文字幕Python Theory for Network Engineers Python允许您构建脚本以自动执行复杂的网络配置 它是用于软件定义网络的最广泛使用的编程语言...
Python Theory for Network Engineers 中文字幕
网络工程师的Python教程 中文字幕Python Theory for Network Engineers
Python允许您构建脚本以自动执行复杂的网络配置 它是用于软件定义网络的最广泛使用的编程语言，并且是新网络工程师的关键技能 本课程讲授使用Python进行网络编程的基础知识 - 理论构建块将导致更好的脚本 学习语言的基础知识，包括对象和变量，字符串，循环和函数 了解如何使用列表，元组和字典，以及集成专门的Python库和模块，如Netmiko和telnetlib 快进，专注于与您相关的主题，或从头到尾观看整个课程，以建立您的核心技能 讲师David Bombal不会让你等到开始自动化网络之前 在此过程中，他展示了如何使用GNS3，Cisco IOS和Python快速轻松地构建基本功能脚本来配置路由器和交换机，以便您可以立即使用新 技能
主题包括： 探索功能和菜单 使用片段 调试 选项和偏好 版本控制
[讲师]现在，在下面的视频中，我将讨论许多Python理论主题。 我已经将这些主题分解为非常短的视频，解释了您可能感兴趣的特定主题。 因此，请使用菜单和大纲来查看您是否感兴趣的特定主题。 您可以将此作为参考用于以后，或者您可以从头到尾处理视频，但我故意将其分解为讨论单个主题的短视频，以便您可以稍后跳转或参考单个主题。 很多这些视频都很短，但尝试讨论一两个主题。 使用理论上的Python主题可能会很无聊，但您必须将这些视为编写更好脚本的构建块。 当您第一次学习TCP / IP或划分子网时，它不一定非常令人兴奋，并且非常复杂并且有时可能令人沮丧，但这种知识在使用网络时非常宝贵。 例如，如果您不了解OSI模型，则很难对网络进行故障排除。 了解OSI或UDP或TCP或IP子网等等并不令人兴奋，但作为网络人员，您必须具备基础知识，并将其视为非常相似的东西。 我们将看一些基本构建块，您可以使用它们为将来的脚本和未来的应用程序开发奠定坚实的基础。 了解有助于您更好地实现代码的工具和机制。 我在这里创建了很多较短的视频，并尝试在这些视频的名称中进行描述，这样如果您想要跳过或者您只对某些主题感兴趣，您可以直接跳转到那些。 但除此之外，只需逐一完成视频打下坚实的基础。 本课程视频下载地址:网络工程师的Python教程
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• Netutils是Python的网络程序员的/类和工具的集合。 它为自定义数据包处理，网络扫描和压力测试提供了不同的类和方法。 一切都在纯Python
• Python is one of the best programming languages out there, with an extensive coverage in scientific computing: computer vision, artificial intelligence, mathematics, astronomy to name a few....
 Python is one of the best programming languages out there, with an extensive coverage in scientific computing: computer vision, artificial intelligence, mathematics, astronomy to name a few. Unsurprisingly, this holds true for machine learning as well.
Of course, it has some disadvantages too; one of which is that the tools and libraries for Python are scattered. If you are a unix-minded person, this works quite conveniently as every tool does one thing and does it well. However, this also requires you to know different libraries and tools, including their advantages and disadvantages, to be able to make a sound decision for the systems that you are building. Tools by themselves do not make a system or product better, but with the right tools we can work much more efficiently and be more productive. Therefore, knowing the right tools for your work domain is crucially important.
This post aims to list and describe the most useful machine learning tools and libraries that are available for Python. To make this list, we did not require the library to be written in Python; it was sufficient for it to have a Python interface. We also have a small section on Deep Learning at the end as it has received a fair amount of attention recently.
We do not aim to list all the machine learning libraries available in Python (the Python package index returns 139 results for “machine learning”) but rather the ones that we found useful and well-maintained to the best of our knowledge. Moreover, although some of modules could be used for various machine learning tasks, we included libraries whose main focus is machine learning. For example, although Scipy has some clustering algorithms, the main focus of this module is not machine learning but rather in being a comprehensive set of tools for scientific computing. Therefore, we excluded libraries like Scipy from our list (though we use it too!).
Another thing worth mentioning is that we also evaluated the library based on how it integrates with other scientific computing libraries because machine learning (either supervised or unsupervised) is part of a data processing system. If the library that you are using does not fit with your rest of data processing system, then you may find yourself spending a tremendous amount of time to creating intermediate layers between different libraries. It is important to have a great library in your toolset but it is also important for that library to integrate well with other libraries.
If you are great in another language but want to use Python packages, we also briefly go into how you could integrate with Python to use the libraries listed in the post.
Scikit-Learn
Scikit Learn is our machine learning tool of choice at CB Insights. We use it for classification, feature selection, feature extraction and clustering. What we like most about it is that it has a consistent API which is easy to use while also providing a lot of evaluation, diagnostic and cross-validation methods out of the box (sound familiar? Python has batteries-included approach as well). The icing on the cake is that it uses Scipy data structures under the hood and fits quite well with the rest of scientific computing in Python with Scipy, Numpy, Pandas and Matplotlib packages. Therefore, if you want to visualize the performance of your classifiers (say, using a precision-recall graph or Receiver Operating Characteristics (ROC) curve) those could be quickly visualized with help of Matplotlib. Considering how much time is spent on cleaning and structuring the data, this makes it very convenient to use the library as it tightly integrates to other scientific computing packages.
Moreover, it has also limited Natural Language Processing feature extraction capabilities as well such as bag of words, tfidf, preprocessing (stop-words, custom preprocessing, analyzer). Moreover, if you want to quickly perform different benchmarks on toy datasets, it has a datasets module which provides common and useful datasets. You could also build toy datasets from these datasets for your own purposes to see if your model performs well before applying the model to the real-world dataset. For parameter optimization and tuning, it also provides grid search and random search. These features could not be accomplished if it did not have great community support or if it was not well-maintained. We look forward to its first stable release.
Statsmodels
Statsmodels is another great library which focuses on statistical models and is used mainly for predictive and exploratory analysis. If you want to fit linear models, do statistical analysis, maybe a bit of predictive modeling, then Statsmodels is a great fit. The statistical tests it provides are quite comprehensive and cover validation tasks for most of the cases. If you are R or S user, it also accepts R syntax for some of its statistical models. It also accepts Numpy arrays as well as Pandas data-frames for its models making creating intermediate data structures a thing of the past!
PyMC
PyMC is the tool of choice for Bayesians. It includes Bayesian models, statistical distributions and diagnostic tools for the convergence of models. It includes some hierarchical models as well. If you want to do Bayesian Analysis, you should check it out.
Shogun
Shogun is a machine learning toolbox with a focus on Support Vector Machines (SVM) that is written in C++. It is actively developed and maintained, provides a Python interface and the Python interface is mostly documented well. However, we’ve found its API hard to use compared to Scikit-learn. Also, it does not provide many diagnostics or evaluation algorithms out of the box. However, its speed is a great advantage.
Gensim
Gensim is defined as “topic modeling for humans”. As its homepage describes, its main focus is Latent Dirichlet Allocation (LDA) and its variants. Different from other packages, it has support for Natural Language Processing which makes it easier to combine NLP pipeline with other machine learning algorithms. If your domain is in NLP and you want to do clustering and basic classification, you may want to check it out. Recently, they introduced Recurrent Neural Network based text representation called word2vec from Google to their API as well. This library is written purely in Python.
Orange
Orange is the only library that has a Graphical User Interface (GUI) among the libraries listed in this post. It is also quite comprehensive in terms of classification, clustering and feature selection methods and has some cross-validation methods. It is better than Scikit-learn in some aspects (classification methods, some preprocessing capabilities) as well, but it does not fit well with the rest of the scientific computing ecosystem (Numpy, Scipy, Matplotlib, Pandas) as nicely as Scikit-learn.
Having a GUI is an important advantage over other libraries however. You could visualize cross-validation results, models and feature selection methods (you need to install Graphviz for some of the capabilities separately). Orange has its own data structures for most of the algorithms so you need to wrap the data into Orange-compatible data structures which makes the learning curve steeper.
PyMVPA
PyMVPA is another statistical learning library which is similar to Scikit-learn in terms of its API. It has cross-validation and diagnostic tools as well, but it is not as comprehensive as Scikit-learn.
Deep Learning
Even though deep learning is a subsection Machine Learning, we created a separate section for this field as it has received tremendous attention recently with various acqui-hires by Google and Facebook.
Theano
Theano is the most mature of deep learning library. It provides nice data structures (tensors) to represent layers of neural networks and they are efficient in terms of linear algebra similar to Numpy arrays. One caution is that, its API may not be very intuitive, which increases learning curve for users. There are a lot of libraries which build on top of Theano exploiting its data structures. It has support for GPU programming out of the box as well.
PyLearn2
There is another library built on top of Theano, called PyLearn2 which brings modularity and configurability to Theano where you could create your neural network through different configuration files so that it would be easier to experiment different parameters. Arguably, it provides more modularity by separating the parameters and properties of neural network to the configuration file.
Decaf
Decaf is a recently released deep learning library from UC Berkeley which has state of art neural network implementations which are tested on the Imagenet classification competition.
Nolearn
If you want to use excellent Scikit-learn library api in deep learning as well, Nolearn wraps Decaf to make the life easier for you. It is a wrapper on top of Decaf and it is compatible(mostly) with Scikit-learn, which makes Decaf even more awesome.
OverFeat
OverFeat is a recent winner of Dogs vs Cats (kaggle competition) which is written in C++ but it comes with a Python wrapper as well(along with Matlab and Lua). It uses GPU through Torch library so it is quite fast. It also won the detection and localization competition in ImageNet classification. If your main domain is in computer vision, you may want to check it out.
Hebel
Hebel is another neural network library comes along with GPU support out of the box. You could determine the properties of your neural networks through YAML files(similar to Pylearn2) which provides a nice way to separate your neural network from the code and quickly run your models. Since it has been recently developed, documentation is lacking in terms of depth and breadth. It is also limited in terms of neural network models as it only has one type of neural network model(feed-forward). However, it is written in pure Python and it will be nice library as it has a lot of utility functions such as schedulers and monitors which we did not see any library provides such functionalities.
Neurolab
NeuroLab is another neural network library which has nice api(similar to Matlab’s api if you are familiar) It has different variants of Recurrent Neural Network(RNN) implementation unlike other libraries. If you want to use RNN, this library might be one of the best choice with its simple API.
Integration with other languages
You do not know any Python but great in another language? Do not despair! One of the strengths of Python (among many other) is that it is a perfect glue language that you could use your tool of choice programming language with these libraries through access from Python. Following packages for respective programming languages could be used to combine Python with other programming languages:
R -> RPythonMatlab -> matpythonJava -> JythonLua -> Lunatic PythonJulia -> PyCall.jl
Inactive Libraries
These are the libraries that did not release any updates for more than one year, we are listing them because some may find it useful, but it is unlikely that these libraries will be maintained for bug fixes and especially enhancements in the future:
MDPMlPyFFnetPyBrain
If we are missing one of your favorite packages in Python for machine learning, feel free to let us know in the comments. We will gladly add that library to our blog post as well.
from: https://www.cbinsights.com/blog/python-tools-machine-learning/
中文版：http://python.jobbole.com/81135/
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• 机器学习 python 什么是机器学习？ (What is Machine Learning?) As the web is immensely growing with each day, analyzing data based on a pattern by human intervention is becoming challenging. To ...

机器学习 python 库

什么是机器学习？ (What is Machine Learning?)
As the web is immensely growing with each day, analyzing data based on a pattern by human intervention is becoming challenging. To leverage this, computer programs are being developed that can analyze data.
由于网络每天都在蓬勃发展，因此通过人工干预基于模式分析数据变得越来越具有挑战性。 为了利用这一点，正在开发可以分析数据的计算机程序。
This allows computers to learn automatically to observe data, look for patterns and make better decisions without much human intervention.
这使计算机可以自动学习观察数据，查找模式并做出更好的决策，而无需人工干预。

This process of making computers to get trained with a given data set to predict the properties of the data is called Machine Learning.
使计算机接受给定数据集的训练以预测数据属性的过程称为机器学习 。
For example, we can train the computer by feeding numerous images of cars to teach the computer to recognize a car. Numerous other images that are not cars can be fed to the computer. From the above training, the computer can recognize the image of a car.
例如，我们可以通过提供大量汽车图像来训练计算机，以教导计算机识别汽车。 可以将许多非汽车图像发送到计算机。 通过以上训练，计算机可以识别汽车的图像。
Machine Learning has earned huge popularity in recent decades and transforming our lives.
机器学习在近几十年来获得了极大的普及，并改变了我们的生活。

为什么要使用Python进行机器学习？ (Why Python for Machine Learning?)
Unlike other computer languages like C, Java, etc., Python is renowned for its readability and less complexity.
与其他计算机语言（例如C，Java等）不同，Python以其易读性和较低的复杂性而闻名。

Anyone can easily understand it and make others to easily understand it, too.
任何人都可以轻松理解它，并使其他人也容易理解它。
Data Scientists can make use of Machine Learning to analyze huge volumes of data and draw useful insights with very less effort.
数据科学家可以利用机器学习来分析大量数据，并以较少的努力得出有用的见解。
Python supports a lot of popular in-built libraries that can be readily used to provide Machine Learning functionality.
Python支持许多流行的内置库，这些库可轻松用于提供机器学习功能。
These libraries have 0 learning curve. Having a basic understanding of Python allows programmers to implement these ready to use libraries.
这些库的学习曲线为0。 对Python有一个基本的了解，使程序员可以实现这些现成的库。
The best part is, these Python packages are free under GNU license.
最好的部分是，这些Python软件包在GNU许可下是免费的。

推荐阅读 ：
Python Data Science Libraries
Python数据科学库

Python机器学习库 (Python Machine Learning Libraries)
Let’s go through some of the commonly used libraries used in the field of Machine Learning.
让我们看一下机器学习领域中使用的一些常用库。
1. NumPy (1. NumPy)
NumPy is a core Python package for performing mathematical and logical operations.
NumPy是用于执行数学和逻辑运算的Python核心软件包。
It supports linear algebra operations and random number generation. NumPy stands for “Numerical Python”.
它支持线性代数运算和随机数生成。 NumPy代表“数字Python”。
NumPy has built-in functions to perform linear algebra operations. NumPy supports multi-dimensional arrays to perform complex mathematical operations. It is essential for fundamental computations in the field of Machine Learning.
NumPy具有内置函数来执行线性代数运算。 NumPy支持多维数组以执行复杂的数学运算。 对于机器学习领域的基础计算而言，它是必不可少的。
2.科学 (2. SciPy)
SciPy is a Python library that is built upon NumPy.
SciPy是基于NumPy构建的Python库。
It makes use of NumPy arrays. SciPy is significantly used for performing advanced operations like regression, integration, and probability.
它利用了NumPy数组。 SciPy大量用于执行高级操作，如回归，积分和概率。
Hence, SciPy is popularly used in the field of Machine Learning as it contains efficient modules for statistics, linear algebra, numerical routines, and optimization.
因此，SciPy包含用于统计，线性代数，数值例程和优化的高效模块，因此在机器学习领域得到了广泛使用。
3. Scikit-Learn (3. Scikit-Learn)
Scikit-Learn is a popular open-source Machine Learning library that is built on top of two famous Python libraries namely NumPy and SciPy.
Scikit-Learn是一种流行的开源机器学习库，它建立在两个著名的Python库NumPy和SciPy之上 。
It features classical ML algorithms for statistical data modeling that includes classification, clustering, regression and preprocessing.
它具有用于统计数据建模的经典ML算法，包括分类，聚类，回归和预处理。
It also provides effective and easy to use Machine Learning tools.
它还提供了有效且易于使用的机器学习工具。
Scikit-learn supports popularly used supervised learning algorithms, as well as unsupervised learning algorithms. The algorithms include support vector machines, grid search, gradient boosting, k-means clustering, DBSCAN and many more.
Scikit-learn支持广泛使用的监督学习算法以及无监督学习算法。 这些算法包括支持向量机，网格搜索，梯度提升，k均值聚类，DBSCAN等。
Along with these algorithms, the kit provides sample datasets for data modeling. The well documented APIs are easily accessible.
该工具包与这些算法一起提供了用于数据建模的样本数据集。 记录良好的API易于访问。
Scikit-learn library is known for its optimal performance across various platforms. This is the reason for its popularity.
Scikit学习库以其在各种平台上的最佳性能而闻名。 这就是它受欢迎的原因。
Hence, it is used for academic and commercial purposes. Scikit-learn is used to build models and it is not recommended to use it for reading, manipulating and summarizing data as there are better frameworks available for the purpose. It is open-source and released under the BSD license.
因此，它用于学术和商业目的。 Scikit-learn用于构建模型，不建议将其用于读取，操作和汇总数据，因为有更好的框架可用于此目的。 它是开源的，并根据BSD许可发布。
4. SymPy (4. SymPy)
SymPy, as the name suggests is a symbolic computation Python library that mainly focuses on algebraic computations.
顾名思义， SymPy是一个符号计算Python库，主要关注代数计算。
Many data scientists use the SymPy library for intermediate mathematical analysis of data. This analysis can be later consumed by other Machine Learning libraries.
许多数据科学家使用SymPy库进行数据的中间数学分析。 以后其他机器学习库可以使用此分析。
5.幕府将军 (5. Shogun)
Shogun is a free, open-source toolbox used for ML that is implemented in C++.
Shogun是用于ML的免费开放源代码工具箱，以C ++实现。
It supports an interface to multiple languages (Python, Java, C#, Ruby, etc) and platforms (Linux, Windows, macOS).
它支持多种语言（Python，Java，C＃，Ruby等）和平台（Linux，Windows，macOS）的接口。

Anyone, be it data scientists, journalists, hackers, students, etc can use Shogun with minimal effort and free of cost.
任何人，无论是数据科学家，新闻工作者，黑客，学生等，都可以以最少的努力和免费使用Shogun。
It provides effective implementation of the standard ML algorithms like SVM, kernel hypothesis, multiple Kernel learning, etc.
它提供了标准ML算法（例如SVM，内核假设，多内核学习等）的有效实现。
Shogun comes along with binary installation packages for scaling multiple systems and hence, provides an extensive testing infrastructure.
Shogun随附用于扩展多个系统的二进制安装包，因此提供了广泛的测试基础结构。
Users can download its docker image and locally run the Shogun cloud. Shogun can scale dozens of OS setups and process about 10 million data samples accurately. Shogun’s cloud is non-commercial and available for educational purposes at universities.
用户可以下载其docker映像并在本地运行Shogun云。 Shogun可以扩展数十种操作系统设置，并可以精确处理约1000万个数据样本。 幕府将军的云是非商业性的，可用于大学的教育目的。
6. TensorFlow (6. TensorFlow)
But, the system is general enough to be applied for a variety of domains. At the year 2015, the library became open source and was released under Apache 2.0 open source license.
但是，该系统足够通用，可以应用于各种领域。 在2015年，该库成为开源库，并根据Apache 2.0开源许可证发布。
TensorFlow is a popular library for dataflow programming. It is a symbolic math library that uses different optimization techniques to make efficient calculations. This Python package is used for the application of Machine Learning and neural network.
TensorFlow是用于数据流编程的流行库。 这是一个符号数学库，它使用不同的优化技术来进行有效的计算。 该Python软件包用于机器学习和神经网络的应用。
TensorFlow provides robust and scalable solutions for computations across numerous machines and for computations involving huge data sets. Hence, it is the preferred framework for Machine Learning.
TensorFlow为跨众多机器的计算以及涉及大量数据集的计算提供了强大且可扩展的解决方案。 因此，它是机器学习的首选框架。
The library is extensible and supports numerous platforms. It provides GPU support for faster computations, improved performance, and better visualization. TensorFlow provides algorithms for classification, estimation models, differentiation, etc.
该库是可扩展的，并支持众多平台。 它提供了GPU支持，可加快计算速度，提高性能并提供更好的可视化效果。 TensorFlow提供用于分类，估计模型，微分等的算法。
TensorFlow provides rich API support for training neural networks and speech recognition using NLP (Natural Language Processing).
TensorFlow提供了丰富的API支持，可使用NLP（自然语言处理）来训练神经网络和语音识别。
7. Theano (7. Theano)
Theano is a numerical computation library primarily used for implementing neural network models.
Theano是一个数值计算库，主要用于实现神经网络模型。
Theano allows to efficiently define, optimize and evaluate mathematical expressions effectively. Theano focusses on solving complex mathematical equations. It uses a multi-dimensional matrix using NumPy to perform these complex operations.
Theano允许有效地定义，优化和评估数学表达式。 Theano专注于求解复杂的数学方程式。 它使用NumPy使用多维矩阵来执行这些复杂的操作。
Theano can find out unstable expressions and replace them with stable ones to evaluate the expressions.
Theano可以找出不稳定的表达式并将其替换为稳定的表达式以评估表达式。
Theano can make effective usage of GPUs. It provides speed optimization by executing parts of expressions in CPU or GPU.
Theano可以有效利用GPU。 它通过在CPU或GPU中执行部分表达式来提供速度优化。
Theano is smart enough to automatically create symbolic graphs for computing gradients and thereby provides symbolic differentiation. Theano is platform-independent.
Theano足够聪明，可以自动创建用于计算梯度的符号图，从而提供符号差异。 Theano与平台无关。
Along with the mentioned features, Theano provides a unit testing platform for error detection.
除了上述功能外，Theano还提供了用于错误检测的单元测试平台。
8. PyTorch (8. PyTorch)
PyTorch is a Python-based scientific computing package targeted for Machine Learning.
PyTorch是针对机器学习的基于Python的科学计算软件包。
It is a replacement for NumPy and provides maximum speed and flexibility by making use of multiple GPUs.
它替代了NumPy，并通过使用多个GPU提供最大的速度和灵活性。
PyTorch also provides custom data loaders and simple preprocessors. PyTorch provides an interactive debugging environment that allows users to debug and visualize effortlessly. It provides an easy to use API.
PyTorch还提供自定义数据加载器和简单的预处理器。 PyTorch提供了一个交互式调试环境，使用户可以轻松进行调试和可视化。 它提供了易于使用的API。
PyTorch support imperative programming. It performs computations on the fly. The biggest benefit of this feature the code and the programming logic is debugged after each line of code.
PyTorch支持命令式编程。 它可以即时执行计算。 此功能的最大好处是在每行代码之后调试代码和编程逻辑。
PyTorch supports dynamic graphs. Instead of using predefined graphs having specific functionalities, PyTorch provides a simple framework to build computational graphs dynamically and also make changes to them during runtime. This is useful in situations where memory requirements for creating a neural network is unknown.
PyTorch支持动态图。 代替使用具有特定功能的预定义图形，PyTorch提供了一个简单的框架来动态构建计算图，并在运行时对其进行更改。 这在创建神经网络的内存需求未知的情况下很有用。
9.凯拉斯 (9. Keras)
Keras is a high-level neural networks API. It is written in Python and can run on top of Theano, TensorFlow or CNTK (Cognitive Toolkit).
Keras是高级神经网络API。 它是用Python编写的，并且可以在Theano，TensorFlow或CNTK（认知工具包）之上运行。
Keras is a user-friendly, extensible and modular library which makes prototyping easy and fast. It supports convolutional networks, recurrent networks and even the combination of both.
Keras是一个用户友好，可扩展的模块化库，可轻松快速地制作原型。 它支持卷积网络，循环网络，甚至两者的组合。
Initial development of Keras was a part of the research of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). It acts as a plugin for other machine learning libraries.
Keras的最初开发是ONEIROS（开放式神经电子智能机器人操作系统）项目研究的一部分。 它充当其他机器学习库的插件。
There are countless deep-learning frameworks available today, but there are some of the areas in which Keras proved better than other alternatives. Keras focuses on minimal user action requirement when common use cases are concerned.
当今有无数的深度学习框架可用，但是Keras在某些领域被证明比其他选择要好。 当涉及到普通用例时，Keras专注于最小的用户操作要求。
For example, if a user makes an error, clear and actionable feedback is provided. This makes Keras easy to learn and use. Hence, Keras is easy-to-use and is the ideal choice for quick prototyping.
例如，如果用户犯了错误，则会提供清晰且可操作的反馈。 这使得Keras易于学习和使用。 因此，Keras易于使用，是快速原型制作的理想选择。
You can easily deploy models to use into other applications very easily, using Keras. Keras also supports multiple backends and allows portability across backends i.e. you can train using one backend and load it with another.
您可以使用Keras轻松地将模型部署到其他应用程序中。 Keras还支持多个后端，并允许跨后端进行可移植性，即，您可以使用一个后端进行培训，然后将其加载到另一个后端。
Keras provides built-in multiple GPU support and supports distributed training.
Keras提供内置的多个GPU支持，并支持分布式培训。

结论 (Conclusion)
In this article, we have discussed the commonly used Python libraries for Machine Learning. Hope this tutorial would help Data Scientists to deep dive into this vast field and make the most out of these Python libraries.
在本文中，我们讨论了用于机器学习的常用Python库。 希望本教程可以帮助数据科学家深入研究这个广阔的领域，并充分利用这些Python库。

翻译自: https://www.journaldev.com/31613/python-machine-learning-libraries

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