東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Statistics with Julia = fundamentals...

Nazarathy, Yoni.

FindBook

Google Book

Amazon

博客來

Statistics with Julia = fundamentals for data science, machine learning and artificial intelligence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistics with Julia/ by Yoni Nazarathy, Hayden Klok.
其他題名:	fundamentals for data science, machine learning and artificial intelligence /
作者:	Nazarathy, Yoni.
其他作者:	Klok, Hayden.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xii, 527 p. :ill., digital ;24 cm.
內容註:	Introducing Julia -- Basic Probability -- Probability Distributions -- Processing and Summarizing Data -- Statistical Inference Concepts -- Confidence Intervals -- Hypothesis Testing -- Linear Regression and Extensions -- Machine Learning Basics -- Simulation of Dynamic Models -- Appendix A: How-to in Julia -- Appendix B: Additional Julia Features -- Appendix C: Additional Packages.
Contained By:	Springer Nature eBook
標題:	Probabilities - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-70901-3
ISBN:	9783030709013

Statistics with Julia = fundamentals for data science, machine learning and artificial intelligence /
Nazarathy, Yoni.

Statistics with Juliafundamentals for data science, machine learning and artificial intelligence /[electronic resource] :by Yoni Nazarathy, Hayden Klok. - Cham :Springer International Publishing :2021. - xii, 527 p. :ill., digital ;24 cm. - Springer series in the data sciences,2365-5682. - Springer series in the data sciences..

Introducing Julia -- Basic Probability -- Probability Distributions -- Processing and Summarizing Data -- Statistical Inference Concepts -- Confidence Intervals -- Hypothesis Testing -- Linear Regression and Extensions -- Machine Learning Basics -- Simulation of Dynamic Models -- Appendix A: How-to in Julia -- Appendix B: Additional Julia Features -- Appendix C: Additional Packages.

This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book's associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With "Statistics with Julia", Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.

ISBN: 9783030709013

Standard No.: 10.1007/978-3-030-70901-3doiSubjects--Topical Terms:

534352
Probabilities
--Data processing.

LC Class. No.: QA273.19.E4 / N39 2021

Dewey Class. No.: 519.2

Statistics with Julia = fundamentals for data science, machine learning and artificial intelligence /
LDR:03564nmm a2200337 a 4500 001 2249375
003 DE-He213
005 20210903231906.0
006 m d
007 cr nn 008maaau
008 220103s2021 sz s 0 eng d
020 $a 9783030709013 $q (electronic bk.)
020 $a 9783030709006 $q (paper)
024 7 $a 10.1007/978-3-030-70901-3 $2 doi
035 $a 978-3-030-70901-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA273.19.E4 $b N39 2021
072 7 $a UFM $2 bicssc
072 7 $a COM077000 $2 bisacsh
072 7 $a UFM $2 thema
082 0 4 $a 519.2 $2 23
090 $a QA273.19.E4 $b N335 2021
100 1 $a Nazarathy, Yoni. $3 3514746
245 1 0 $a Statistics with Julia $h [electronic resource] : $b fundamentals for data science, machine learning and artificial intelligence / $c by Yoni Nazarathy, Hayden Klok.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xii, 527 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in the data sciences, $x 2365-5682
505 0 $a Introducing Julia -- Basic Probability -- Probability Distributions -- Processing and Summarizing Data -- Statistical Inference Concepts -- Confidence Intervals -- Hypothesis Testing -- Linear Regression and Extensions -- Machine Learning Basics -- Simulation of Dynamic Models -- Appendix A: How-to in Julia -- Appendix B: Additional Julia Features -- Appendix C: Additional Packages.
520 $a This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book's associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With "Statistics with Julia", Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
650 0 $a Probabilities $x Data processing. $3 534352
650 0 $a Statistics $x Data processing. $3 535534
650 0 $a Julia (Computer program language) $3 3270502
650 1 4 $a Mathematical Software. $3 897499
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
650 2 4 $a Data Structures. $3 891009
650 2 4 $a Probability and Statistics in Computer Science. $3 891072
700 1 $a Klok, Hayden. $3 3514747
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer series in the data sciences. $3 2165646
856 4 0 $u https://doi.org/10.1007/978-3-030-70901-3
950 $a Mathematics and Statistics (SpringerNature-11649)