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Titlebook: Machine Learning with Python; Theory and Implement Amin Zollanvari Textbook 2023 The Editor(s) (if applicable) and The Author(s), under exc

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發(fā)表于 2025-3-21 16:34:17 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning with Python
副標題Theory and Implement
編輯Amin Zollanvari
視頻videohttp://file.papertrans.cn/621/620717/620717.mp4
概述This textbook focuses on the most essential elements and practically useful techniques in Machine Learning.Strikes a balance between the theory of Machine Learning and implementation in Python.Supplem
圖書封面Titlebook: Machine Learning with Python; Theory and Implement Amin Zollanvari Textbook 2023 The Editor(s) (if applicable) and The Author(s), under exc
描述This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of machine learning from both a theoretical and a practical perspective. The choice of the topics in the book is made based on one criterion: whether the practical utility of a certain method justifies its theoretical elaboration for students with a typical mathematical background in engineering and other quantitative fields. As a result, not only does the book contain practically useful techniques, it also presents them in a mathematical language that is accessible to both graduate and advanced undergraduate students.?.The textbook covers a range of topics including nearest neighbors, linear models, decision trees, ensemble learning, model evaluation and selection,?dimensionality reduction, assembling various learning stages, clustering, and deep learning along with an introduction to fundamental Python packages for data science and machine learning such as NumPy, Pandas, Matplotlib, Scikit-Learn, XGBoost, and Keras with TensorFlow backend.?.Given the current dominant role of the Python programming language for machine learning, the book complements the theoret
出版日期Textbook 2023
關(guān)鍵詞Keras-TensorFlow; Clustering; Convolutional Neural Networks; Decision Trees; Deep Learning; Ensemble Lear
版次1
doihttps://doi.org/10.1007/978-3-031-33342-2
isbn_softcover978-3-031-33344-6
isbn_ebook978-3-031-33342-2
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
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https://doi.org/10.1007/978-3-031-33342-2Keras-TensorFlow; Clustering; Convolutional Neural Networks; Decision Trees; Deep Learning; Ensemble Lear
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k-Nearest Neighbors, kNN (short for k-Nearest Neighbors) not only because of its simplicity, but also due to its long history in machine learning. As a result, in this chapter we formalize the kNN mechanism for both classification and regression and we will see various forms of kNN.
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發(fā)表于 2025-3-22 19:49:37 | 只看該作者
Amin Zollanvarie excitations, the excitonic polaritons. It helps to obtain evidence for the predictions made by Hopfield and others of some extraordinary properties of these objects. Let us mention, for instance, the dispersive, multiple Brillouin peaks, the AS/S ratio in RBS larger than unity, the disappearence o
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發(fā)表于 2025-3-22 23:55:36 | 只看該作者
Amin Zollanvariented of experimental Raman scattering studies and their interpretation based on models of the lattice dynamics of pristine and intercalated graphite. The periodic layer structure of intercalation compounds makes it possible to model the dynamical matrix by a Brillouin zone folding of the pristine g
9#
發(fā)表于 2025-3-23 05:26:25 | 只看該作者
Amin Zollanvarie excitations, the excitonic polaritons. It helps to obtain evidence for the predictions made by Hopfield and others of some extraordinary properties of these objects. Let us mention, for instance, the dispersive, multiple Brillouin peaks, the AS/S ratio in RBS larger than unity, the disappearence o
10#
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