Top Python-Based Deep Learning Packages: A Comprehensive Review
DOI:
https://doi.org/10.47667/ijpasr.v5i1.283Keywords:
Python-Based Deep Learning, Deep Learning Packages, Python Deep Learning LibrariesAbstract
Deep learning has transformed artificial intelligence (AI) by empowering machines to execute intricate functions with unparalleled precision. The field claims an array of robust packages and libraries, among which Python, a prominent and celebrated programming language, has emerged as a pivotal choice for deep learning study and development. Python has become a leading language in deep learning due to its simplicity and the vast array of libraries available for developers and researchers. This article thoroughly examines the most broadly adopted deep learning packages within the Python system. The packages under scrutiny include TensorFlow, PyTorch, Keras, Theano, and Caffe. We exactly assess each of these packages to establish their typical features and capabilities. Moreover, the review explores into an in-depth analysis of the assets and weaknesses inherent in each package. This detailed exploration prepares readers with the information necessary to make informed decisions regarding the variety of the most suitable packages custom-made to their specific needs. This comprehensive review aims to propose a nuanced understanding of the landscape of popular deep learning packages and support practitioners and researchers in creation strategic and well-informed choices for their deep learning actions.
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