找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Beginning Mathematica and Wolfram for Data Science; Applications in Data Jalil Villalobos Alva Book 2024Latest edition Jalil Villalobos Alv

[复制链接]
查看: 42688|回复: 48
发表于 2025-3-21 17:53:55 | 显示全部楼层 |阅读模式
期刊全称Beginning Mathematica and Wolfram for Data Science
期刊简称Applications in Data
影响因子2023Jalil Villalobos Alva
视频video
发行地址The first introduction to data science using Mathematica and Wolfram.Covers popular in-demand topics such as machine learning, neural networks, and new LLM functionalities.Includes freely available so
图书封面Titlebook: Beginning Mathematica and Wolfram for Data Science; Applications in Data Jalil Villalobos Alva Book 2024Latest edition Jalil Villalobos Alv
影响因子.Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization...You’ll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. 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. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data.  ..You’ll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you’ll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mi
Pindex Book 2024Latest edition
The information of publication is updating

书目名称Beginning Mathematica and Wolfram for Data Science影响因子(影响力)




书目名称Beginning Mathematica and Wolfram for Data Science影响因子(影响力)学科排名




书目名称Beginning Mathematica and Wolfram for Data Science网络公开度




书目名称Beginning Mathematica and Wolfram for Data Science网络公开度学科排名




书目名称Beginning Mathematica and Wolfram for Data Science被引频次




书目名称Beginning Mathematica and Wolfram for Data Science被引频次学科排名




书目名称Beginning Mathematica and Wolfram for Data Science年度引用




书目名称Beginning Mathematica and Wolfram for Data Science年度引用学科排名




书目名称Beginning Mathematica and Wolfram for Data Science读者反馈




书目名称Beginning Mathematica and Wolfram for Data Science读者反馈学科排名




单选投票, 共有 0 人参与投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 21:10:30 | 显示全部楼层
发表于 2025-3-22 00:40:53 | 显示全部楼层
发表于 2025-3-22 07:54:27 | 显示全部楼层
发表于 2025-3-22 12:02:42 | 显示全部楼层
Metal Catalysed Reactions in Ionic Liquidslized functions of the Wolfram Language for the same purpose, using statistical functions. The Wolfram Language is a useful tool for statistics and probability. Mathematica has the functions to perform numerical and approximate calculations for descriptive statistics and random distributions, random
发表于 2025-3-22 15:30:59 | 显示全部楼层
发表于 2025-3-22 20:36:32 | 显示全部楼层
Carbon-Carbon Coupling Reactions,, how to use the commands for different layers, and the most common layers. You learn how to enter data into the layers by the net port and the different forms of equivalent expression of the layers. This topic is followed by how to distinguish different layers by their symbol. You see that layers c
发表于 2025-3-22 22:37:52 | 显示全部楼层
https://doi.org/10.1007/979-8-8688-0348-2programming; data science; Wolfram; Mathematica; language; big data; machine learning; cloud; analytics; codi
发表于 2025-3-23 04:44:56 | 显示全部楼层
发表于 2025-3-23 08:51:28 | 显示全部楼层
Jalil Villalobos AlvaThe first introduction to data science using Mathematica and Wolfram.Covers popular in-demand topics such as machine learning, neural networks, and new LLM functionalities.Includes freely available so
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-30 12:17
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表