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Record Nr. |
UNISA996391837403316 |
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Autore |
Pomroy John |
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Titolo |
A faithful discovery of a treacherous design of mystical Antichrist displaying Christs banners, but attempting to lay waste Scriptures, churches, Christ, faith, hope, &c. and establish paganism in England [[electronic resource] ] : Seasonably given in a letter to the faithful in and near to Beverly, / / by Joseph Kellet, John Pomroy, and Paul Glisson. Containing an examination of many doctrines of the people called Quakers in Yorkshire, and now in most parts of England: together with a censure of their way, and several items concerning the designs of God, Satan, and men, in these things, recommended to the consideration of them who are in good earnest for Christ, By Christopher Feak. John Simpson. George Cokayn |
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Pubbl/distr/stampa |
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London, : Printed for Thomas Brewster, and are to be sold at the Three Bibles in Pauls Church-yard, 1655 [i.e. 1654] |
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Descrizione fisica |
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Altri autori (Persone) |
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FeakeChristopher <fl. 1645-1660.> |
GlissonPaul |
KelletJoseph |
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Soggetti |
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Society of Friends |
Familists |
Christian life |
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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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Note generali |
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Authors' note "To the reader" signed: Joseph Kellet, John Pomroy, Paul Glissen. |
"An advertisement to the reader" signed: Christopher Feak. John Simpson. George Cokayn. Lawrence Wise. |
Thomason received his copy 18 December 1654. |
Annotation on Thomason copy: "Decemb. 18. 1654"; the final '5' in the imprint has been crossed out. |
With two final advertisement leaves. |
Reproduction of the original in the British Library. |
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2. |
Record Nr. |
UNINA9910137429003321 |
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Titolo |
Workload Characterization (IISWC), 2015 IEEE International Symposium on |
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Materiale a stampa |
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3. |
Record Nr. |
UNINA9911015638303321 |
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Autore |
Sun Yi |
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Titolo |
A Mathematical Introduction to Data Science / / by Yi Sun, Rod Adams |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (XIV, 476 p. 10 illus.) |
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Disciplina |
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Soggetti |
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Artificial intelligence - Data processing |
Computer science - Mathematics |
Machine learning |
Mathematical statistics |
Artificial intelligence |
Data Science |
Mathematics of Computing |
Machine Learning |
Probability and Statistics in Computer Science |
Artificial Intelligence |
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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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Chapter 1 Introduction -- Chapter 2 Sets and Functions -- Chapter 3 Liner Algebra -- Chapter 4 Matrix Decomposition -- Chapter 5 Calculus -- Chapter 6 Advanced Calculus -- Chapter 7 Algorithms 1 – Principal Component Analysis -- Chapter 8 Algorithms 2 – Liner Regression -- Chapter 9 Algorithms 3 – Neural Networks -- Chapter 10 Probability -- Chapter 11 Further Probability -- Chapter 12 Elements of Statistics -- Chapter 13 Algorithms 4 – Maximum Likelihood Estimation and its Application to Regression -- Chapter 14 Data Modelling in Practice. |
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Sommario/riassunto |
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This textbook provides a comprehensive foundation in the mathematics needed for data science for students and self-learners with a basic mathematical background who are interested in the principles behind computational algorithms in data science. It covers sets, functions, linear algebra, and calculus, and delves deeply into probability and statistics, which are key areas for understanding the algorithms driving modern data science applications. Readers are guided toward unlocking the secrets of algorithms like Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression, illuminating the path from mathematical principles to algorithmic mastery. It is designed to make the material accessible and engaging, guiding readers through a step-by-step progression from basic mathematical concepts to complex data science algorithms. It stands out for its emphasis on worked examples and exercises that encourage active participation, making it particularly beneficial for those with limited mathematical backgrounds but a strong desire to learn. This approach facilitates a smoother transition into more advanced topics. The authors expect readers to be proficient in handling numbers in various formats, including fractions, decimals, percentages, and surds. They should also have a knowledge of introductory algebra, such as manipulating simple algebraic expressions, solving simple equations, and graphing elementary functions, along with a basic understanding of geometry including angles, trigonometry and Pythagoras’ theorem. |
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