Python is one of the most widely used languages among programmers. There are plenty of reasons why it has become popular recently: programming syntax simplicity and a large collection of libraries are one of them. Since Python is easy in use, it’s attractive for developers to create new libraries for machine learning. Are you interested in knowing more about them? In this article, we prepared a list of the best Python libraries.
This Python library is fast in speed and flexible in operability. Also, you can use TensorFlow in every Google application which is effective for writing complicated algorithms with diverse operations. The other advantage of this library is neural networks that could be displayed with computational graphs. Apart from it, TensorFlow has N-dimensional matrices for representing your data. If you want to visualize your data, TensorFlow is a great option for you.
Do you eager to develop a scrawling program? If you do so, Scrapy is the best option for you! First off, it was created for scraping, and its name indicates this fact. Scrapy is a fabulous opportunity for you to provide Python development services, data mining, and automated testing. Additionally, Scrapy is an open-source library that you can use freely and download it on your computer device.
It’s a Python library that contains a lot of supervised and unsupervised learning algorithms. Scikit-Learn is the best tool for dealing with a complex data. You can also do many changes in this library: from cross-validation feature to logistics regression if you use a training method. The features of Scikit-Learn that make it valuable for programmers are cross-validation, feature extraction, and unsupervised learning algorithms. All in all, this Python library includes many algorithms that might help you cope with data mining, machine learning, clustering, reducing dimension, and model selection.
It’s one of the most popular machine learning library for Python among developers. Numpy contains various interfaces, and you can perform many operations on TensorFlow. What are the other benefits of Numpy for programmers? So simple use of this Python library and ability to make mathematical expressions easily are one of them. Also, Numpy has an intuitive feature that eases the way you code. If you’re a full stack developer who wants to expand machine learning knowledge, this library is definitely for you!
Keras is a machine learning library that contains a simple mechanism of neural networks. It includes the necessary utilities for you like data visualization, compiling models, and data-sets. When we compare with the other machine learning libraries, Keras creates computational graphs, including a backend infrastructure and model operations. Additionally, it supports all types of neural networks: from pooling to embedding that establishes a core background for products. Do you know that Uber and Netflix are built with Keras? If you don’t, here is the answer for you: these companies used it for creating an interactive interface for clients. Developers prefer to work with Keras because it has different blocks as layers, optimizers, and activation function.
This Python library allows developers to use tensor computations with GPU acceleration. PyTorch is also considered to be the biggest machine learning for programming. Since its creation back in 2017, this library is becoming more and more popular nowadays. Its optimized performance and research introduction are beneficial for collective operations in Python and C++. When you build a rich ecosystem of the tools, PyTorch is a suitable option for you! It supports the application that you want to use for natural language processing. Facebook and Uber implement it for their programming processes.