In the last article we covered some common libraries for numerical computation, data visualization, Web development, and database management. Let’s take a look at common libraries for automated operations, graphical interface programming, machine learning, and deep learning.

Automated operation and maintenance

Jumpsever detecting

An open source jumbler (Fortress) system written in Python that implements the basic functions of jumblers, including authentication, authorization, and auditing, and integrates Ansible, batch commands, and more. Support WebTerminalBootstrap writing, beautiful interface, automatic collection of hardware information, support video playback, command search, real-time monitoring, batch upload and download and other functions, based on SSH protocol for management, the client does not need to install agent. Primarily used to address visual security management, it is easy to develop again because it is completely open source.

Magedu distributed monitoring system

An automated monitoring system developed in Python that monitors common system services, applications, and network devices. It can monitor many different services on a single host. Different services can have different monitoring intervals. You can monitor the same service on different hosts. Intervals and alert thresholds can be different and provide a data visualization interface.

The CMDB Magedu

The hardware management system developed in Python includes three functions: collecting hardware data, API and page management. It is mainly used to automatically manage the daily use of common devices such as laptops and routers. The client terminal of the server collects hardware data and sends hardware information to the API. The API is responsible for storing the obtained data in the database, and the hypervisor is responsible for configuring and displaying the server information.

User interface (GUI) programming

Tkinter

A Python standard GUI library for quickly creating GUI applications that can be used on most Unix platforms, as well as Windows and Mac OS systems. Later versions of Inter8.0 implement native window styles and run well on most platforms.

wxPython

WxWidgets Python package and Python module is an open source software cross-platform GUI library. It is an excellent GUI graphical library written in Python, making it easy to create complete and functional GUI user interfaces.

PyQt

The tool library for creating GUI applications is a successful fusion of the Python programming language and Qt. It runs on all major operating systems, including UNIX, Windows, and Mac. PyQt uses a dual license. Developers can choose between the GPL and commercial licenses. Starting with PyQt release 4, the GPL license is available on all supported platforms.

PySide

The Cross-platform application framework Qt is a Python bound version that provides similar functionality to PyQt and is compatible with the API, but uses a different LGPL license than PyQt.

Machine learning

Scikit-Learn

Scikit-learn is based on NumPy and SciPy. It is a Python module built specifically for machine learning. It provides a number of tools for data mining and analysis, including a series of interfaces, such as data preprocessing, cross-validation, algorithms, and visualization algorithms.

Orange3

Orange3 is a component-based data mining and machine learning package that supports Script development in Python. It contains a range of data visualization, retrieval, preprocessing, and modeling techniques, has a good user interface, and can be used as a Python module.

Users can analyze data through data visualization, Includes statistical distribution maps, histograms, scatter plots and deeper decision trees, hierarchical clustering, heat maps, MDS (multidimensional analysis), linear prediction, etc., and can use Orange with a variety of other functional components for NLP, text mining, network analysis, inferential high-frequency datasets and association rule data analysis.

XGBoost

XGBoost is a machine learning library that focuses on gradient lifting algorithms. It has attracted extensive attention for its excellent learning effect and effective training speed. XGBoost supports parallel processing and is more than 10 times better than the Scikit-Learn library, which also implements gradient enhancement algorithms. XGBoost can handle multiple tasks, such as regression, sorting, and sorting.

NuPIC

NuPIC is a machine learning platform focused on time series. Its core algorithm is HTM algorithm, which is closer to the operating structure of human brain than deep learning. The theoretical basis of HTM algorithm is mainly the working principle of the neocortex, which processes the higher cognitive functions in the human brain. NuPIC can be used for prediction and anomaly detection and has a wide range of applications requiring only input time series.

Milk (MachineLearningToolkit)

Milk focuses on speed and memory footprint, so most performance-sensitive code is written in C ++ and provides a Python interface on top of that for ease of use. It focuses on providing supervised classification methods, such as SVM, KNN, random forest and decision tree, and also supports unsupervised learning algorithms, such as K-means and tight relation propagation.

Deep learning

Caffe (ConvolutionalArchitectureforFastFeatureEmbedding)

This is a deep learning framework with expression, speed and modularity at its core. It has the characteristics of high definition, readability and speed, and is widely used in video and image processing. Network architecture and optimizations in Caffe are defined in the form of configuration files that are easy to use without the need to build a network through code. The network training speed is fast, can train the big data set and the latest model; Modular components can be easily extended to new models and learning tasks.

Theano

Theano, born in 2008, is a high-performance symbolic computing and deep learning library. It is regarded as one of the ancestors of deep learning libraries and one of the important standards for deep learning research and application. Its core is a mathematical expression compiler designed to handle large-scale neural network training calculations. Theano is well integrated with NumPy and can use NumPy’s DARray directly, thus greatly reducing the cost of learning the API interface; Its calculation stability is good, can accurately calculate the output value of small functions, such as log (1 + x); Dynamically generate C or CUDA code to compile into efficient machine code.

TensorFlow

TensorFlow is a relatively advanced machine learning library. Its core code is written in C ++ and supports automatic derivation, making it easy for users to design neural network structures without having to write THEIR own C ++ or CUDA code or solve gradients through back propagation. Because the underlying layer is written in THE C ++ language, it ensures operational efficiency and simplifies the complexity of online deployment.

Keras

Keras is a highly modular neural network library, implemented in Python, that runs on both TensorFlow and Theano.

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