東華大學圖書館 |

Machine learning algorithms using Scikit and TensorFlow environments

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning algorithms using Scikit and TensorFlow environments/ Puvvadi Baby Maruthi, Amit Kumar Tyagi, Smrity Prasad, editors.
其他作者:	Tyagi, Amit Kumar.
出版者:	Hershey, Pennsylvania :IGI Global, : 2024.,
面頁冊數:	1 online resource (xx, 453 p.) :ill. (chiefly col.)
內容註:	Chapter 1. Classification models in machine learning techniques -- Chapter 2. Machine learning algorithm with tensorflow and scikit for next generation systems -- Chapter 3. Understanding convolutional neural network with tensorflow: CNN -- Chapter 4. A deep understanding of long short-term memory for solving vanishing error problem: LSTM-VGP -- Chapter 5. Coffee leaf diseases classification using deep learning approach -- Chapter 6. COVID-19 classification with healthcare images based on ML-DL methods -- Chapter 7. Unravelling the enigma of machine learning model interpretability in enhancing disease prediction -- Chapter 8. Deep learning for the intersection of ethics and privacy in healthcare -- Chapter 9. Early detection of Alzheimer's using artificial intelligence for effective emotional support systems -- Chapter 10. Malware analysis and classification using machine learning models -- Chapter 11. Improved breast cancer detection in mammography images: integration of convolutional neural network and local binary pattern approach -- Chapter 12. Predicting depression from social media users by using lexicons and machine learning algorithms -- Chapter 13. Mental stress detection using bidirectional encoder representations from transformers -- Chapter 14. SCRNN: a deep model for colorectal cancer classification from histological images - implementation using tensorflow -- Chapter 15. SRAM memory testing methods and analysis: an approach for traditional test algorithms to ML models -- Chapter 16. Imagining the sustainable future with industry 6.0: a smarter pathway for modern society and manufacturing industries -- Chapter 17. Dew computing: state of the art, opportunities, and research challenges -- Chapter 18. The future of artificial intelligence in blockchain applications -- Chapter 19. Transformative effects of chatgpt on the modern era of education and society: from society's and industry's perspectives -- Chapter 20. Using ensemble learning and random forest techniques to solve complex problems.
標題:	Machine learning. -
電子資源:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-8531-6
ISBN:	9781668485330

Machine learning algorithms using Scikit and TensorFlow environments
Machine learning algorithms using Scikit and TensorFlow environments[electronic resource] /Puvvadi Baby Maruthi, Amit Kumar Tyagi, Smrity Prasad, editors. - Hershey, Pennsylvania :IGI Global,2024. - 1 online resource (xx, 453 p.) :ill. (chiefly col.)

Includes bibliographical references and index.

Chapter 1. Classification models in machine learning techniques -- Chapter 2. Machine learning algorithm with tensorflow and scikit for next generation systems -- Chapter 3. Understanding convolutional neural network with tensorflow: CNN -- Chapter 4. A deep understanding of long short-term memory for solving vanishing error problem: LSTM-VGP -- Chapter 5. Coffee leaf diseases classification using deep learning approach -- Chapter 6. COVID-19 classification with healthcare images based on ML-DL methods -- Chapter 7. Unravelling the enigma of machine learning model interpretability in enhancing disease prediction -- Chapter 8. Deep learning for the intersection of ethics and privacy in healthcare -- Chapter 9. Early detection of Alzheimer's using artificial intelligence for effective emotional support systems -- Chapter 10. Malware analysis and classification using machine learning models -- Chapter 11. Improved breast cancer detection in mammography images: integration of convolutional neural network and local binary pattern approach -- Chapter 12. Predicting depression from social media users by using lexicons and machine learning algorithms -- Chapter 13. Mental stress detection using bidirectional encoder representations from transformers -- Chapter 14. SCRNN: a deep model for colorectal cancer classification from histological images - implementation using tensorflow -- Chapter 15. SRAM memory testing methods and analysis: an approach for traditional test algorithms to ML models -- Chapter 16. Imagining the sustainable future with industry 6.0: a smarter pathway for modern society and manufacturing industries -- Chapter 17. Dew computing: state of the art, opportunities, and research challenges -- Chapter 18. The future of artificial intelligence in blockchain applications -- Chapter 19. Transformative effects of chatgpt on the modern era of education and society: from society's and industry's perspectives -- Chapter 20. Using ensemble learning and random forest techniques to solve complex problems.

"Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow.Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students."--

Mode of access: World Wide Web.

ISBN: 9781668485330Subjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

533906
Machine learning.
Subjects--Index Terms:

Artificial Neural Networks.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: Q325.5 / .M32132 2024eb

Dewey Class. No.: 006.3/1

Machine learning algorithms using Scikit and TensorFlow environments
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504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Classification models in machine learning techniques -- Chapter 2. Machine learning algorithm with tensorflow and scikit for next generation systems -- Chapter 3. Understanding convolutional neural network with tensorflow: CNN -- Chapter 4. A deep understanding of long short-term memory for solving vanishing error problem: LSTM-VGP -- Chapter 5. Coffee leaf diseases classification using deep learning approach -- Chapter 6. COVID-19 classification with healthcare images based on ML-DL methods -- Chapter 7. Unravelling the enigma of machine learning model interpretability in enhancing disease prediction -- Chapter 8. Deep learning for the intersection of ethics and privacy in healthcare -- Chapter 9. Early detection of Alzheimer's using artificial intelligence for effective emotional support systems -- Chapter 10. Malware analysis and classification using machine learning models -- Chapter 11. Improved breast cancer detection in mammography images: integration of convolutional neural network and local binary pattern approach -- Chapter 12. Predicting depression from social media users by using lexicons and machine learning algorithms -- Chapter 13. Mental stress detection using bidirectional encoder representations from transformers -- Chapter 14. SCRNN: a deep model for colorectal cancer classification from histological images - implementation using tensorflow -- Chapter 15. SRAM memory testing methods and analysis: an approach for traditional test algorithms to ML models -- Chapter 16. Imagining the sustainable future with industry 6.0: a smarter pathway for modern society and manufacturing industries -- Chapter 17. Dew computing: state of the art, opportunities, and research challenges -- Chapter 18. The future of artificial intelligence in blockchain applications -- Chapter 19. Transformative effects of chatgpt on the modern era of education and society: from society's and industry's perspectives -- Chapter 20. Using ensemble learning and random forest techniques to solve complex problems.
520 3 $a "Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow.Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students."-- $c Provided by publisher.
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650 0 $a Computer algorithms. $3 523872
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653 $a Classification.
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653 $a Decision Tree.
653 $a Ensemble Learning.
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653 $a Scikit.
653 $a Support Vector Machine.
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700 1 $a Maruthi, Puvvadi Baby, $d 1986- $3 3793510
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