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Titlebook: Machine Learning with R; Abhijit Ghatak Textbook 2017 Springer Nature Singapore Pte Ltd. 2017 Overfitting and underfitting.Bias-Variance t

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樓主
發(fā)表于 2025-3-21 17:53:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning with R
編輯Abhijit Ghatak
視頻videohttp://file.papertrans.cn/621/620719/620719.mp4
概述Help readers understand the mathematical interpretation of learning algorithms.Teach the basics of linear algebra, probability, and data distributions and how they are essential in formulating a learn
圖書封面Titlebook: Machine Learning with R;  Abhijit Ghatak Textbook 2017 Springer Nature Singapore Pte Ltd. 2017 Overfitting and underfitting.Bias-Variance t
描述.This book helps readers understand the mathematics of ?machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it’s applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning..In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation..The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readerscan modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning a
出版日期Textbook 2017
關(guān)鍵詞Overfitting and underfitting; Bias-Variance trade off; Regularization; Optimization; Gradient descent/as
版次1
doihttps://doi.org/10.1007/978-981-10-6808-9
isbn_softcover978-981-13-4950-8
isbn_ebook978-981-10-6808-9
copyrightSpringer Nature Singapore Pte Ltd. 2017
The information of publication is updating

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發(fā)表于 2025-3-22 00:17:05 | 只看該作者
Textbook 2017the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it’s applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. T
板凳
發(fā)表于 2025-3-22 04:11:21 | 只看該作者
ystems. We have seen that the method can be used to measure the excitations associated with the free electron motion in the plane as well as that of the restricted motion normal to the plane. These features, in conjunction with the very advantageous option to measure spectra of collective and single
地板
發(fā)表于 2025-3-22 08:16:56 | 只看該作者
Abhijit Ghatakystems. We have seen that the method can be used to measure the excitations associated with the free electron motion in the plane as well as that of the restricted motion normal to the plane. These features, in conjunction with the very advantageous option to measure spectra of collective and single
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We expect important applications in the area of time-resolved spectroscopy, where the method could reveal dynamical behavior of hot electrons. There is increasing interest in systems where the electrons have one-dimensional behavior (quantum wires) and also in zero-dimensional systems (quantum dots
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發(fā)表于 2025-3-22 23:57:49 | 只看該作者
Abhijit Ghatak We expect important applications in the area of time-resolved spectroscopy, where the method could reveal dynamical behavior of hot electrons. There is increasing interest in systems where the electrons have one-dimensional behavior (quantum wires) and also in zero-dimensional systems (quantum dots
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