Beginning Mathematica and Wolfram for data science : applications in data analysis, machine learning, and neural networks
著者
書誌事項
Beginning Mathematica and Wolfram for data science : applications in data analysis, machine learning, and neural networks
Apress Media, c2021
- : pbk
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注記
Includes index
内容説明・目次
内容説明
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.
What You Will Learn
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
Who This Book Is For
Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language.
目次
1. Introductiona. What is Data science?b. Data science and Statisticsc. Data scientist
2. Introduction to Mathematicaa. Why Mathematica?b. Wolfram Languagec. Structure of Mathematicad. Notebooks e. How Mathematica worksf. Input Form
3. Data Manipulation a. Listsb. Lists of objectsc. Manipulating listsd. Operations with listse. Indexed Tablesf. Working with data framesg. Datasets
4. Data Analysisa. Data Import and exportb. Wolfram data repositoryc. Statistical Analysisd. Visualizing datae. Making reports
5. Machine learning with Wolfram Languagea. Linear Regressionb. Multiple Regressionc. Logistic Regressiond. Decision Tresse. Data Clustering
6. Neural networks with Wolfram Languagea. Network Data and structureb. Network Layersc. Perceptron Modeld. Multi-layer Neural Networke. Using preconstructed nets from Wolfram Neural net repositoryf. LeNet Neural net for text recognition
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