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Record Nr. |
UNISA996387973603316 |
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Autore |
Brunschwig Hieronymus <ca. 1450-ca. 1512.> |
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Titolo |
The vertuose boke of the distyllacyon of all maner of waters of the herbes in this present volume expressed [[electronic resource] ] : with the fygures of the stillatoryes to that noble worke belongynge / / fyrst made and compyled by the thirtye yeres studye and labour of the moste famous and expert master of phisyke, Master Iherom Bruynswyke ... ; and nowe of late newly translated into Englysshe out of Duche by me Laurence Andrew . |
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Pubbl/distr/stampa |
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[London], : Imprynted at London in the Flete strete by me Laurens Andrewe in th sygne of the Golden Crosse, M.ccccc.xxvii, the xviii daye of Apryll [18 Apr. 1527] |
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Descrizione fisica |
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Altri autori (Persone) |
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AndrewLaurence <fl. 1510-1537.> |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Imprint from colophon. |
Signatures: [par.]⁴ a-b⁶ c-d⁴ e-f⁶ A-B⁴ C⁶ D⁴ E⁶ F⁴ G⁶ H⁴ I⁶ K⁴ L⁶ M⁴ N⁶ O⁴ P⁶ Q⁴ R⁶ S-T⁴ U⁶ X⁴. |
T.p. contains illustration. |
Imperfect: print show-through. |
Reproduction of original in the Harvard University. Library. |
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Sommario/riassunto |
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2. |
Record Nr. |
UNINA9910984669403321 |
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Autore |
Wang Zhiyuan |
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Titolo |
Numerical Machine Learning |
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Pubbl/distr/stampa |
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Sharjah : , : Bentham Science Publishers, , 2023 |
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©2023 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (225 pages) |
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Altri autori (Persone) |
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IrfanSayed Ameenuddin |
TeohChristopher |
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Soggetti |
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Machine learning |
Numerical analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover
-- Title
-- Copyright
-- End User License Agreement
-- Content
-- Preface
-- Introduction to Machine Learning
-- Linear Regression -- Regularization
-- Logistic Regression
-- Decision Tree
-- Gradient Boosting
-- Support Vector Machine
-- K-means Clustering
-- Subject Index |
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Sommario/riassunto |
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Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, |
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logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses. |
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