Python continues to be a leader in operating in machine learning and deep learning, artificial intelligence, and data science. The programming community is baffled by Python’s rapid growth and impact, and its numerous application possibilities have made it much simpler for novices and newcomers in the field to pick Python as the primary programming language they can master. Because of its wide-ranging application within computing science, many Python libraries have been developed, which have proved to be the most popular in the world of deep learning and machine learning experts.
This article identifies the top Python deep learning library available to programmers in 2022.
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TensorFlow is an open-source software library that supports deep-learning applications developed in collaboration with the Google Brain Team. The initial idea was to use it for numerical computations; the library now offers an extensive, flexible, and wide array of libraries, tools, and community-based resources developers can utilize to develop and implement machine learning-based software. TensorFlow 2.5.0 was released in 2015, the first version launched in 2015; it was recently upgraded with the help of the Google Brain team to include new capabilities.
PyTorch is among the most well-known open-source deep-learning libraries developed in 2016 by Facebook’s AI research team. The name of this library is derived from the famous deep-learning framework Torch, scientific scripting, and computation software written by the Lua programming language. PyTorch allows you to perform deep learning tasks and build a computers-base vision and NLP applications.
Keras is a well-known open-source library used primarily to perform tasks related to deep learning. It facilitates the rapid testing of deep neural networks. Francois Chollet created it, and it first came out in the year 2015. Keras is a tool for building visualizations of graphs and analyzing datasets. It also has prelabeled data that can be imported directly and loaded. It’s easy to use, versatile and perfect for research.
DBSCAN and gradient boosting support vector machines and random forests are just a few of the regression, classification, and clustering strategies included in SciKit-Learn. For the traditional data mining and ML, David Cournapeau designed the library built on SciPy, NumPy, and Matplotlib.
It is no doubt that NumPy is among the most well-known Python libraries, which can be utilized for large multidimensional arrays and matrix processing using an extensive collection of high-level mathematical operations. It is crucial for efficient scientific calculations in machine learning, and it is handy for linear algebra and other functions.
SciPy is an entirely free and open-source software library that is an extension of NumPy. It is among the most popular Python libraries which can be used for technical and scientific computation with large data sets. Built-in modules support SciPy to optimize arrays and linear algebra.
It is among the most open-source Python libraries mainly employed to support Data Science and machine learning areas. The library primarily provides data manipulation and analysis tools utilized for data analysis through its robust data structures to manipulate tables of numbers and calculations of the time series.
It is a highly scalable open-source Deep learning framework developed to build and implement deep neural networks. It can perform fast model training and is compatible with various programming languages, including C, C++, Python, Julia, Matlab, etc.
CNTK (Cognitive Toolkit), previously called Computational Network Toolkit and released by Microsoft in 2016, is an open-source deep-learning library designed to enable distributed deep machine learning and learning tasks. It is possible to combine the most widely used predictive models, like CNN (Convolutional Neural Network) as well as the feed-forward DNN (Deep Neural Network) and RNN (Recurrent Neural Network) and the CNTK framework to complete end-to-end deep-learning tasks.
Theano is an algorithm for computational Python library explicitly designed for deep and machine learning libraries. It facilitated efficient design, optimization, and evaluation of mathematical equations and matrix calculations, which use multidimensional arrays to build deep learning models.