by Aratrika Dutta
April 28, 2022
If you are a programmer, these Python libraries are for you! Master them to perform well in your work
Python continues to lead the way in machine learning, artificial intelligence, deep learning, and data science. The programming world is perplexed by Python’s growth and influence, and its vast use cases make it even easier for beginners and those new to the field to choose Python as their first programming language to learn. Thanks to its widespread implementation in the computing world, several Python libraries have emerged that have proven to be the most popular among machine learning and deep learning professionals. In this article, we have listed the best Python deep learning libraries for programmers in 2022.
TensorFlow is an open source library for deep learning applications created by the Google Brain team. Originally designed for numerical computations, it now provides a rich, flexible, and extensive set of tools, libraries, and community resources that developers can use to build and deploy machine learning-based applications. TensorFlow 2.5.0, which was first released in 2015, has just been updated by the Google Brain team to include new features.
PyTorch is one of the most popular open-source deep learning libraries, created in 2016 by Facebook’s AI research team. The name of the respective library is based on the popular Torch deep learning framework, a scientific computing and scripting tool written in the Lua programming language. PyTorch allows you to implement deep learning tasks and allows you to build computer vision and NLP applications.
Keras is a well-known open source library which is mainly used for deep learning related tasks. It allows rapid testing of deep neural networks. François Chollet created it and it was initially launched in 2015. Keras provides tools to build models, visualize graphs and analyze datasets. It also includes pre-tagged datasets that can be directly imported and loaded. It is simple to use, adaptable and well suited to an exploratory study.
DBSCAN, gradient boosting, support vector machines, and random forests are some of the classification, regression, and clustering methods included in SciKit-Learn. For conventional ML and data mining applications, David Cournapeau designed the library in addition to SciPy, NumPy and Matplotlib.
Undoubtedly, NumPy is one of the most popular Python libraries that can be used seamlessly for processing large multi-dimensional arrays and matrices, using a large collection of high-level mathematical functions. It is very important for efficient fundamental scientific computations in machine learning and is especially useful for linear algebra and other operations.
SciPy is a free and open source library based on NumPy. It is one of the best Python libraries that can be used to perform scientific and engineering calculations on large data sets. SciPy comes with built-in modules for array optimization and linear algebra.
It is one of the open source Python libraries which is mainly used in the fields of data science and machine learning. This library mainly provides data manipulation and analysis tools, which are used to analyze data using its powerful data structures for manipulating numerical arrays and time series analysis.
It is a highly scalable open-source deep learning framework designed to train and deploy deep neural networks. It is able to train models quickly and supports multiple programming languages such as C, C++, Python, Julia, Matlab, etc.
CNTK (Cognitive Toolkit), formerly known as Computational Network ToolKit and released by Microsoft in 2016, is an open source deep learning library used to implement distributed deep learning and machine learning tasks. You can easily combine the most popular predictive models, such as CNN (Convolutional Neural Network), feed-forward DNN (Deep Neural Network) and RNN (Recurrent Neural Network), with the CNTK framework to effectively implement end-to-end learning tasks.
Theano is a numerical computation Python library created specifically for machine learning and deep libraries. It enabled efficient definition, optimization and evaluation of mathematical expressions and matrix calculations to use multidimensional arrays to create deep learning models.
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