標(biāo)題: Titlebook: Beginning Mathematica and Wolfram for Data Science; Applications in Data Jalil Villalobos Alva Book 20211st edition Jalil Villalobos Alva 2 [打印本頁] 作者: 適婚女孩 時間: 2025-3-21 16:16
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書目名稱Beginning Mathematica and Wolfram for Data Science讀者反饋學(xué)科排名
作者: 彈藥 時間: 2025-3-22 00:19
A Hypergame Analysis for ErsatzPasswordsto the associations, how to associate keys with values and understand that they are fundamental for the correct construction of datasets in the Wolfram Language. We conclude with a final overview on how associations are abstract constructions of hierarchical data.作者: homocysteine 時間: 2025-3-22 04:07 作者: 散布 時間: 2025-3-22 07:23 作者: 小鹿 時間: 2025-3-22 11:45
Working with Data and Datasets,to the associations, how to associate keys with values and understand that they are fundamental for the correct construction of datasets in the Wolfram Language. We conclude with a final overview on how associations are abstract constructions of hierarchical data.作者: 詼諧 時間: 2025-3-22 16:53 作者: Presbycusis 時間: 2025-3-22 19:33
Data Exploration, the data from this platform through the use of the Wolfram Language as well as its representation of data in the form dataset as well as using the Query command. We will also look at how data can be viewed inside datasets, how to apply user functions, and commands inside the format dataset.作者: 清晰 時間: 2025-3-22 21:39
Book 20211st editionok 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 sci作者: palliative-care 時間: 2025-3-23 02:17
Divya Hiran,Hemant Kothari,Shivoham Singhfor these functions. In each case, we will explain which parts of the model are fundamental for the correct construction using the Wolfram Language. For this part of the book we will use examples of known datasets such as the Fisher‘s Irises dataset, Boston housing dataset, and the Titanic dataset.作者: 可商量 時間: 2025-3-23 07:53
Machine Learning with the Wolfram Language,for these functions. In each case, we will explain which parts of the model are fundamental for the correct construction using the Wolfram Language. For this part of the book we will use examples of known datasets such as the Fisher‘s Irises dataset, Boston housing dataset, and the Titanic dataset.作者: 強所 時間: 2025-3-23 13:10
Introduction to Mathematica,e and time formats, simple plots of functions, logical operators, performance measures, delayed expressions, and accessing Wolfram alpha. We will then look at how Mathematica performs code computation, distinguishing between how to enter code in different forms of input, showing how to see what Math作者: Medicaid 時間: 2025-3-23 14:33
Neural Networks with the Wolfram Language,tax forms. Next we will view the concepts of encoders and decoders and how these tools are used for the construction of a neural network model, depending on the task to fulfill. We then learn how these encoders and decoders are used to convert different data types to numeric arrays, as well as how t作者: oncologist 時間: 2025-3-23 20:05
Book 20211st editionto 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 exploredata and describe the concepts using Wolfram language c作者: 離開 時間: 2025-3-23 22:11
Beginning Mathematica and Wolfram for Data ScienceApplications in Data作者: blight 時間: 2025-3-24 05:38
Beginning Mathematica and Wolfram for Data Science978-1-4842-6594-9作者: CANE 時間: 2025-3-24 07:44 作者: 整頓 時間: 2025-3-24 14:25
Ashish P. Ramdasi,S. Sathyalakshmitax forms. Next we will view the concepts of encoders and decoders and how these tools are used for the construction of a neural network model, depending on the task to fulfill. We then learn how these encoders and decoders are used to convert different data types to numeric arrays, as well as how t作者: Minutes 時間: 2025-3-24 17:37 作者: 健談的人 時間: 2025-3-24 22:16 作者: 凹處 時間: 2025-3-25 00:15 作者: cortex 時間: 2025-3-25 05:01
Working with Data and Datasets,roughout a notebook; and how to write code in one of the powerful syntax used in the Wolfram Language called pure functions. Naturally, we will pass, to the associations, how to associate keys with values and understand that they are fundamental for the correct construction of datasets in the Wolfra作者: 旅行路線 時間: 2025-3-25 08:07 作者: 和平主義者 時間: 2025-3-25 12:50
Data Exploration, built in order to have a better understanding of its use in Mathematica through the Wolfram Language. Examples will be carried out on how to download the data from this platform through the use of the Wolfram Language as well as its representation of data in the form dataset as well as using the Qu作者: PET-scan 時間: 2025-3-25 16:05 作者: 苦笑 時間: 2025-3-25 20:41 作者: Lumbar-Stenosis 時間: 2025-3-26 02:16 作者: 胎兒 時間: 2025-3-26 04:26 作者: Entreaty 時間: 2025-3-26 09:05 作者: CLEAR 時間: 2025-3-26 14:05 作者: abreast 時間: 2025-3-26 19:12 作者: Minatory 時間: 2025-3-26 21:15
Jatinderkumar R. Saini,Vikas S. Chomalugh equations and implement specialized functions of the Wolfram Language for the same purpose. With the use of 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作者: Ringworm 時間: 2025-3-27 01:39
Atul Kumar Verma,Mahipal Jadeja,Rahul Saxena built in order to have a better understanding of its use in Mathematica through the Wolfram Language. Examples will be carried out on how to download the data from this platform through the use of the Wolfram Language as well as its representation of data in the form dataset as well as using the Qu作者: 恩惠 時間: 2025-3-27 08:31
Divya Hiran,Hemant Kothari,Shivoham Singhutations will be shown as well as the concept of the learning curve of the model. Later, we will see how to use the specialized functions of the Wolfram Language for machine learning such as Predict, Classify and ClusterClassify, in the case of linear regression, for logistic regression and for clus作者: 清真寺 時間: 2025-3-27 09:51
Ashish P. Ramdasi,S. Sathyalakshmiayers, how to use the commands for different layers, and the most common layers. We will learn how to enter data into the layers by the net port, as well as the different forms of equivalent expression of the layers. This is followed by how to distinguish different layers by their symbol. We will se作者: 王得到 時間: 2025-3-27 15:29 作者: 鍵琴 時間: 2025-3-27 18:56
Jalil Villalobos AlvaThe first introduction to data science using Mathematica and Wolfram.Covers very popular in-demand topics such as machine learning and neural networks.Includes freely available source code作者: Derogate 時間: 2025-3-28 01:01 作者: 用樹皮 時間: 2025-3-28 03:53
ICT Systems Security and Privacy Protectione define what can be included within this structure as well as the creation of lists, nested lists, arrays, vectors, and matrices. We explore how to order a list, how to assign new values, and finally how to select elements of a list depending on an established pattern.作者: WITH 時間: 2025-3-28 08:54 作者: 吞噬 時間: 2025-3-28 12:32 作者: 使出神 時間: 2025-3-28 17:13
Kayode Odusanya,Olu Aluko,Banita LalIn this section, we will see how to train a neural network model in the Wolfram Language, how to access the results, and the trained network. We will review the basic commands to export and import a net model. We end the chapter with the exploration of the Wolfram Neural Net Repository and the review of the LeNet network model.作者: 古董 時間: 2025-3-28 20:38 作者: 反應(yīng) 時間: 2025-3-29 00:11
Data Visualization,In this chapter we will see more depth in terms of data visualization, where we will see the different ways of representing data visually, with the use of different commands, and create a range of different types of graphs. We will also see how to customize plots and use predefined plot themes.作者: BORE 時間: 2025-3-29 03:45 作者: Calculus 時間: 2025-3-29 09:38 作者: neologism 時間: 2025-3-29 15:10
Book 1966 been chosen, for the most part, from the Held of analysis of the mechanical properties of steel, wood, and other materials. A necessary prerequisite for any study of correlation equations is so me knowledge of the moments of random variables. In the Appendix, there is provided a brief treatment of
