Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/11/4/193 |
| _version_ | 1797571392804225024 |
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| author | Sebastian Raschka Joshua Patterson Corey Nolet |
| author_facet | Sebastian Raschka Joshua Patterson Corey Nolet |
| author_sort | Sebastian Raschka |
| collection | DOAJ |
| description | Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward. |
| first_indexed | 2024-03-10T20:39:47Z |
| format | Article |
| id | doaj.art-50c2ea63f8a14b54b90c49e8b301b789 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T20:39:47Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-50c2ea63f8a14b54b90c49e8b301b7892023-11-19T20:43:59ZengMDPI AGInformation2078-24892020-04-0111419310.3390/info11040193Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial IntelligenceSebastian Raschka0Joshua Patterson1Corey Nolet2Department of Statistics, University of Wisconsin-Madison, Madison, WI 53575, USANVIDIA, Santa Clara, CA 95051, USANVIDIA, Santa Clara, CA 95051, USASmarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.https://www.mdpi.com/2078-2489/11/4/193Pythonmachine learningdeep learningGPU computingdata scienceneural networks |
| spellingShingle | Sebastian Raschka Joshua Patterson Corey Nolet Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence Information Python machine learning deep learning GPU computing data science neural networks |
| title | Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence |
| title_full | Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence |
| title_fullStr | Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence |
| title_full_unstemmed | Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence |
| title_short | Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence |
| title_sort | machine learning in python main developments and technology trends in data science machine learning and artificial intelligence |
| topic | Python machine learning deep learning GPU computing data science neural networks |
| url | https://www.mdpi.com/2078-2489/11/4/193 |
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