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

Beginning Mathematica and Wolfram fo...

Villalobos Alva, Jalil.

FindBook

Google Book

Amazon

博客來

Beginning Mathematica and Wolfram for data science = applications in data analysis, machine learning, and neural networks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Beginning Mathematica and Wolfram for data science/ by Jalil Villalobos Alva.
其他題名:	applications in data analysis, machine learning, and neural networks /
作者:	Villalobos Alva, Jalil.
出版者:	Berkeley, CA :Apress : : 2021.,
面頁冊數:	xxiii, 416 p. :ill., digital ;24 cm.
內容註:	1. Introduction to Mathematica -- 2. Data Manipulation -- 3. Working with Data and Datasets -- 4. Import and Export -- 5. Data Visualization -- 6. Statistical Data Analysis -- 7. Data Exploration -- 8. Machine Learning with the Wolfram Language -- 9. Neural Networks with the Wolfram Language -- 10. Neural Network Framework.
Contained By:	Springer Nature eBook
標題:	Mathematica (Computer program language) -
電子資源:	https://doi.org/10.1007/978-1-4842-6594-9
ISBN:	9781484265949

Beginning Mathematica and Wolfram for data science = applications in data analysis, machine learning, and neural networks /
Villalobos Alva, Jalil.

Beginning Mathematica and Wolfram for data scienceapplications in data analysis, machine learning, and neural networks /[electronic resource] :by Jalil Villalobos Alva. - Berkeley, CA :Apress :2021. - xxiii, 416 p. :ill., digital ;24 cm.

1. Introduction to Mathematica -- 2. Data Manipulation -- 3. Working with Data and Datasets -- 4. Import and Export -- 5. Data Visualization -- 6. Statistical Data Analysis -- 7. Data Exploration -- 8. Machine Learning with the Wolfram Language -- 9. Neural Networks with the Wolfram Language -- 10. Neural Network Framework.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You'll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You'll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you'll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. You will: Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering.

ISBN: 9781484265949

Standard No.: 10.1007/978-1-4842-6594-9doiSubjects--Topical Terms:

535917
Mathematica (Computer program language)

LC Class. No.: QA76.73.M29 / V555 2021

Dewey Class. No.: 510.285536

Beginning Mathematica and Wolfram for data science = applications in data analysis, machine learning, and neural networks /
LDR:02928nmm a2200325 a 4500 001 2237792
003 DE-He213
005 20210630171251.0
006 m d
007 cr nn 008maaau
008 211111s2021 cau s 0 eng d
020 $a 9781484265949 $q (electronic bk.)
020 $a 9781484265932 $q (paper)
024 7 $a 10.1007/978-1-4842-6594-9 $2 doi
035 $a 978-1-4842-6594-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.M29 $b V555 2021
072 7 $a U $2 bicssc
072 7 $a COM000000 $2 bisacsh
072 7 $a UX $2 thema
082 0 4 $a 510.285536 $2 23
090 $a QA76.73.M29 $b V714 2021
100 1 $a Villalobos Alva, Jalil. $3 3490311
245 1 0 $a Beginning Mathematica and Wolfram for data science $h [electronic resource] : $b applications in data analysis, machine learning, and neural networks / $c by Jalil Villalobos Alva.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xxiii, 416 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction to Mathematica -- 2. Data Manipulation -- 3. Working with Data and Datasets -- 4. Import and Export -- 5. Data Visualization -- 6. Statistical Data Analysis -- 7. Data Exploration -- 8. Machine Learning with the Wolfram Language -- 9. Neural Networks with the Wolfram Language -- 10. Neural Network Framework.
520 $a Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You'll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You'll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you'll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. You will: Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering.
650 0 $a Mathematica (Computer program language) $3 535917
650 0 $a Wolfram language (Computer program language) $3 3490312
650 0 $a Mathematics $x Data processing. $3 524921
650 1 4 $a Professional Computing. $3 3201325
650 2 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6594-9
950 $a Professional and Applied Computing (SpringerNature-12059)