Main features of PyTorch:
- support for a simple and easy-to-use interface;
- tons of other tools and libraries that can be used with PyTorch;
- good compatibility with distributive learning;
- the ability to integrate with Cython and Numba.
As mentioned above, PyTorch is a direct rival to Tensor Flow, which means it can successfully act as an alternative to the latter. It is popular with specialists because it can be exploited to process natural language applications (NLP). In this area, PyTorch has achieved much better performance than the previously mentioned competitor.
Gradient Boosting is one of the best known machine learning libraries to help developers create new algorithms. There are special libraries that make it possible to quickly and efficiently implement this method, one of which is LightGBM.
The features of the library include:
- fast computation and high performance;
- intuitive and user-friendly interface;
- learning faster than in other similar libraries;
- there is no possibility of error in the case of NaN values.
Binary and multiclassification support, scalability and optimization support make LightGBM quite popular with developers.
A machine learning library called Eli5 was created to eliminate inaccurate results from different prediction models. It is engaged in tracking algorithms, contributes to the debugging of training models using a variety of visualizations.
Main features of Eli5:
- support for other libraries, including: XGBoost, lightning;
- support for debugging training classifiers;
- helping developers understand the reason for any prediction.
Eli5 is used for mathematical operations in order to reduce the time of the latter. It is also used for legacy applications and various Python-dependent packages.
It is a machine learning library representing high-level data structures and a wide range of analysis tools. A distinctive feature of Pandas is the ability to transfer the most complex operations with information using just one or two commands. This library contains many ways to combine data, group and filter it.
Features of Pandas include:
- the ability to simplify data manipulation;
- support for sorting, visualization and other options.
Pandas provides a lot of flexibility and functionality when used with other libraries. In addition, users can use it in various operations, data sorting applications, etc.
Seaborn uses other libraries (NumPy, Pandas) and is used for data visualization. It contains graph plotting options based on datasets.
The features and benefits of Seaborn include:
- aggregation and display - functions built into the library to help create graphs;
- high-level interface for building statistical graphs (for easy understanding and study of data);
- diagrams and pie charts, histograms, error messages;
- tools for choosing color gamma - to find patterns in the data.
Seaborn is in demand among regression models. It is effective for creating a variety of Matplotlib-style themes and for plotting time series statistic data. It is also used to visualize uni- and bivariate data.
Using this library, it is possible to create various graphics at the same time (including two-dimensional). It is also used for operations with figures. Engineers and developers often use Matplotlib to work and edit quality drawings. The teaching of this library is quite simple and straightforward, which adds it popularity.
Features of Matplotlib:
- the ability to use with different tools (graphical interfaces and Python scripts, Jupyter notebooks, Ipython shells);
- integration with Seaborn;
- the ability to develop high quality images for publications;
- printed formats of pictures.
If the user needs help, there is a huge community
dedicated to this library. There you can get comprehensive information and learning Matplotlib.
The use of this library is quite extensive. These are object-oriented APIs for embedded charting in a variety of applications. Also Matplotlib introduces the pylab procedural interface, supports different toolsets (Excel and natgrid, cartography).
The PIL or Python Image Library section was first available with the Python Image structure and codes. However, after several years of research, a more convenient, optimal coding method was developed. It is said in the Python community that Pillow is nothing more than a modern take on PIL. Overall, it is a very user-friendly and reliable image processing platform.
Let's take a look at the most interesting features of Pillow:
- the ability to create sketches;
- image filters;
- compatibility with GIF, PNG, PDF and other formats;
- the ability to make changes to images.
It is possible to use Pillow to find out information about an image. It handles blurry pictures efficiently. The library is operated for batch processing and image archiving applications.
This is not only another Python library, but also a computer ad compiler that supports a lot of mathematical operations - analysis, optimization, etc. In addition, Theano provides support for optimal exploitation of multidimensional arrays.
Features of Theano:
- speed and ensuring stable optimization;
- the ability to perform fast calculations with an impressive amount of information;
- support for effective symbolic differentiation;
- automatic detection of errors and malfunctions.
This library is in demand for use in deep learning of a neural network. It can also be used for software development
This library is considered one of the best for working with complex data. It integrates with NumPy and SciPy. Scikit-Learn is frequently modified and improved. One of such optimizations is cross-validation option that allows you to apply more than one metric.
Features of Scikit-Learn:
- the ability to extract elements from texts and pictures;
- cross-validation - many different methods of checking the accuracy of the controlled model on invisible information;
- many machine learning algorithms and the ability to implement expensive tasks (clustering, factor analysis, etc.).
Scikit-Learn is used to implement common machine learning and data mining tasks.
Some useful features of the library include:
- the ability to get the results of work in a notebook (HTML media);
- available to easily convert Matplotlib visualizations;
- simple commands are used to build statistical scenarios;
- custom visualizations can be used to implement the style.
This library supports the Bokeh server. It can be used to transform a large amount of information. Different graphs can be updated with automatic streaming.
Popular Python library for computer face recognition, image processing. It regulates all kinds of options related to instant computer vision. It is believed that due to the lack of documentation, OpenCV is difficult to learn. However, due to the fact that it comes with most of the pre-written built-in options, it is quite possible to learn how to process images.